phd online machine learning

Machine Learning (Ph.D.)

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

GW Online Engineering Programs

• Artificial Intelligence and Machine Learning
• Cloud Computing Management
• Cybersecurity Analytics
• Cybersecurity Policy & Compliance
• Electrical Engineering
• Engineering Management
• Systems Engineering
• Policies & Procedures
• On-Site International Programs
• Artificial Intelligence & Machine Learning
• Degrees Offered
• Tuition & Financial Aid
• Student Resources
• Application Process
• Apply for Admission

Doctoral Degrees

GW’s Online Engineering Programs offer a number of post-master’s online doctoral degree programs: the Doctor of Engineering (D.Eng.) in Artificial intelligence & machine learning, the Doctor of Engineering (D.Eng.) in cybersecurity analytics, and the Doctor of Engineering (D.Eng.) in engineering management, and the Doctor of Philosophy (Ph.D.) in systems engineering. The George Washington University Online Engineering Doctoral Programs can empower you to address the sizable challenges facing our modern world. Regardless of your interests and career goals, our online doctoral programs can open doors and advance your career in nearly any field.

Completion of the Doctor of Engineering programs requires completion of eight courses and a research praxis. The research in Doctor of Engineering is applied and involves the solution of a real-world problem using the latest engineering concepts and tools. Through applied (rather than basic) research, the student creates an advanced, practice-based solution. The expected duration of the program is 2 years.

The Doctor of Philosophy program requires completion of eight courses and a dissertation, which leads to foundational work and contributes to the field of systems engineering. The expected duration of the program is 3 years.

By pursuing a Doctoral degree online, you can enjoy the many professional and personal benefits of graduate education, including:

• Contributing to your organization’s innovation and growth
• Proving your advanced knowledge, widening your career path and its trajectory
• Increasing your levels of influence, expertise, and responsibility
• Earning higher compensation and increased job stability

To learn more about our online doctoral programs, use the links provided below:

Doctor of Engineering (D.Eng.) in Artificial Intelligence and Machine Learning

Doctor of Engineering (D.Eng.) in Cybersecurity Analytics  

Doctor of Engineering (D.Eng.) in Engineering Management

Doctor of Philosophy (Ph.D.) in Systems Engineering

Graduate Education

Office of graduate and postdoctoral education, machine learning (ml), program contact.

Stephanie Niebuhr Georgia Institute of Technology 801 Atlantic Drive Atlanta, GA 30332-0405

Application Deadlines

Application deadline varies by home school.

• Aerospace Engineering: April 1
• Biomedical Engineering: December 1
• Electrical and Computer Engineering: December 16
• Industrial & Systems Engineering: December 15
• Mathematics: December 15
• School of Chemical & Biomolecular Engineering: December 15
• School of Computational Science & Engineering: December 15
• School of Computer Science: December 15
• School of Interactive Computing: December 15

Admittance Terms

Degree programs.

• PhD, Machine Learning

Areas of Research

Our world-class faculty and students specialize in areas including, but not limited to:

• Computer Vision
• Natural Language Processing
• Deep Learning
• Game Theory
• Neuro Computing
• Ethics and Fairness
• Artificial Intelligence
• Internet of Things
• Machine Learning Theory
• Systems for Machine Learning
• Bioinformatics
• Computational Finance
• Health Systems
• Information Security
• Logistics and Manufacturing

Interdisciplinary Programs

The Machine Learning Ph.D. is an interdisciplinary doctoral program spanning three colleges (Computing, Engineering, Sciences).  Students are admitted through one of eight participating home schools:

• Computer Science (Computing)
• Computational Science and Engineering (Computing)
• Interactive Computing (Computing)– see  Computer Science
• Aerospace Engineering (Engineering)
• Biomedical Engineering (Engineering)
• Electrical and Computer Engineering (Engineering)
• Mathematics (Sciences)
• Industrial Systems Engineering (Engineering)

Admission to the ML PhD program is contingent on meeting the requirement for admission into one of these schools. It is possible that, due to space or other constraints, that you are admitted to the general PhD program in your home school but not the ML PhD program.

The ML PhD program is a cohesive, interdisciplinary course of study subject to a unique set of curriculum requirements; see the program webpage for more information.

Standardized Tests

IELTS Academic Requirements

• Varies among home units.

TOEFL Requirements

GRE Requirements

Application Requirements

Please note that application requirements may vary by home unit, including the application deadlines and test score requirements, as well as support for incoming students (including guarantees of teaching assistantships and/or fellowships). Please review the home unit links above or contact them directly for details.

Program Costs

• Go to " View Tuition Costs by Semester ," and select the semester you plan to start. Graduate-level programs are divided into sections: Graduate Rates–Atlanta Campus, Study Abroad, Specialty Graduate Programs, Executive Education Programs
• Find the degree and program you are interested in and click to access the program's tuition and fees by credit hour PDF.
• In the first column, determine the number of hours (or credits) you intend to take for your first semester.
• Determine if you will pay in-state or out-of-state tuition. Learn more about the difference between in-state and out-of-state . For example, if you are an in-state resident and planning to take six credits for the Master of Architecture degree, the tuition cost will be $4,518.
• The middle section of the document lists all mandatory Institute fees. To see your total tuition plus mandatory fees, refer to the last two columns of the PDF.

Program Links

The Office of Graduate Education has prepared an admissions checklist to help you navigate through the admissions process.

Ph.D. in Computer Science

Gain vital expertise to lead and innovate with the help of invaluable "practice experience" in a fast-paced, real-world environment.

Through critical and logical thinking, you’ll gain the essential knowledge and experience needed to become highly proficient in the use of today’s leading computing platforms and techniques.

Why earn a Ph.D. in computer science?

If you're an international student, refer to the international application process for deadlines.

Scientists and engineers in every industry rely on high-performance technology and large data sets, requiring experts that can help harness the latest sophisticated computing power to solve real-world problems.

With this graduate program, you'll:

• Get essential "practice experience" to help solve real-world problems and challenges through computational technology
• Develop the knowledge and skills that will prepare you to lead or support research in any technical career that relies on computer science.
• Develop your logic and critical-thinking skills to help solve today's most pressing scientific and engineering challenges.
• Choose from computation clusters focused on specialized computing system or methods, and application clusters for exposure to specific scientific disciplines.
• Work with practitioners in a variety of disciplines served by computer science .

On-Campus or Online Ph.D. in Computer Science

Benefit from strong departmental proficiencies in artificial intelligence, compiler design, database, networks, operating systems, graphics, simulation, software engineering, and theoretical computer science.

Shape the future of transportation. UND’s Transportation Technology Research Initiative is using autonomous systems to develop and maintain a modern transportation system.

Advance your technology skills with a curriculum that encourages a formal, abstract, theoretical and practical approach to the study of computer science.

Gain access to on-campus computer power: two computer labs, a set of diverse servers and a high-performance computing (HPC) system.  The supercomputer at UND runs on the HPE Apollo 6500 Gen10 system, purpose-built for HPC and a leading platform for deep learning. 

UND is a leader in big data expertise. We are the lead institution in a multi-university project for digital agriculture, funded by the National Science Foundation . And we  co-lead another NSF project to determine industry and academic computational needs in the Midwest.

Study at a Carnegie Doctoral Research Institution ranked #151 by the NSF. Students are an integral part of UND research.

What can I do with a Ph.D. in computer science?

Anticipated job growth for computer and information research scientists through 2032

U.S. Bureau of Labor Statistics

Median annual salary for computer and information research scientists, 2023

Graduates of the Computer Science Ph.D. program have dynamic career paths with titles such as:  

• Software engineer and developer
• Computational scientist
• Data science engineer
• Research scientists (technology companies and universities)

Because technology systems are so essential today, UND graduates can expect career opportunities across a range of industries. A small sampling of top industries needing advanced scientific computing skills include:

• Atmospheric science
• Bioinformatics
• Communications
• Engineering and science
• High tech (hardware)
• Renewable energy
• Scientific and medical research (private and university-level)
• Software engineering and design

Ph.D. in Computer Science Courses

CSCI 515. Data Engineering and Management. 3 Credits.

This course studies theoretical and applied research issues related to data engineering, management, and science. Topics will reflect state-of-the-art and state-of-the-practice activities in the field. The course focuses on well-defined theoretical results and empirical studies that have potential impact on data acquisition, analysis, indexing, management, mining, retrieval, and storage. Prerequisite: CSCI 513 . S, even years.

CSCI 543. Machine Learning. 3 Credits.

An introductory course in machine learning for data science. Topics include the learning algorithms of a Bayesian network, neural network, parametric/non-parametric methods, kernel machine, support-vector machine, etc. for regression, classification, clustering, dimensionality reduction, etc. Prerequisite: CSCI 365 or CSCI 384 . F, odd years.

CSCI 567. Secure Software Engineering. 3 Credits.

This course covers software engineering principles and techniques used in the development life-cycle of cyber secure systems. Topics covered include, the characteristics of secure software, the role of security in the development life-cycle, designing secure software, and best-practices in secure programming and testing. Study includes review of industrial standards for secure software system engineering. Prerequisite: EE 601 , EE 602 , and admission to the MS Cyber Security Program. SS.

CSCI 554. Applications in AI/Computational Intelligence. 3 Credits.

A continuous study of the computational paradigms of Soft Computing in the field of Computational Intelligence. The topics include the applications of the various soft computing techniques in Computational Intelligence as well as more evolutionary algorithms in Swarm Intelligence. Prerequisite: CSCI 544 . F, even years.

CSCI 555. Computer Networks. 3 Credits.

A study of new and developing network architectures and communication protocols. Broadband technologies will be considered including BISDN, ATM networks, and other high-speed networks. Prerequisite: CSCI 327 .

CSCI 557. Computer Forensics. 3 Credits.

An overview of the techniques to detect and assess the level of penetration of a security breach. Topics include forensic science in the cyber domain, laws and ethics of forensic activities, digital evidence, methods of forensic investigation, and forensic procedures in a variety of operating systems and network configurations. Prerequisite: EE 602 , or approval of the department, and admission to the MS program in Cyber Security. S.

Online Computer Science Ph.D.

best online graduate programs

best online college in North Dakota

Intelligent

UND's online Ph.D. in Computer Science is fully online. You never have to come to campus. You'll take a combination of synchronous and asynchronous online computer science courses. 

Affordable Online Colleges

UND is one of the most affordable online colleges in the region. For this program, we offer the same online tuition rates regardless of your legal residency. Compare and you’ll see UND is lower cost than similar four-year doctoral universities.

Top-Tier Online Computer Science Ph.D.

Over a third of UND's student population is exclusively online; plus, more take a combination of online and on campus classes. You can feel reassured knowing you won't be alone in your online learning journey and you'll have resources and services tailored to your needs. No matter how you customize your online experience, you’ll get the same top-quality education as any other on campus student.

• Same degree:  All online programs are fully accredited by the Higher Learning Commission (HLC) . Your transcript and diploma are exactly the same as our on-campus students.
• Same classes: You’ll take courses from UND professors, start and end the semesters at the same time and take the same classes as a student on campus.
• Real interaction:  You can ask questions, get feedback and regularly connect with your professors, peers and professionals in the field.
• Your own academic advisor:  As an invaluable go-to, they’re focused on you, your personal success and your future career.
• Free online tutoring:  We're here to help you one-on-one at no cost. Plus, get access to a variety of self-help online study resources.
• Unlimited academic coaching:  Need support to achieve your academic goals or feeling stumped by a tough course? We'll help with everything from stress and time management to improving your memory to achieve higher test scores.
• Full online access: Dig into virtual research at UND's libraries. Improve your writing skills with online help from the UND Writing Center. Get online access to career services, veteran and military services, financial services and more.
• 24/7 technical support:  UND provides free computer, email and other technical support for all online students.
• Networking opportunities: Our significant online student population means you’ll have a large pool of peers to connect with. UND has numerous online events and activities to keep you connected.

Best Online College

Our high alumni salaries and job placement rates, with affordable online tuition rates make UND a best-value university for online education. UND's breadth of online programs rivals all other nonprofit universities in the Upper Midwest making UND one of the best online schools in the region.

UND ranks among the best online colleges in the nation for:

• Affordability
• Student satisfaction (retention rate)
• Academic quality (4-year graduate rate)
• Student outcomes (20-year return on investment per Payscale.com)

Leaders in Computer Science

As a leader of Big Data, UND's goal is to make things more efficient, more effective and safer for North Dakotans.

Check out the faculty you'll work with at UND or discover additional education opportunities.

• School of Electrical Engineering & Computer Science
• Find Similar Programs

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Doctor of Philosophy with a major in Machine Learning

The Doctor of Philosophy with a major in Machine Learning program has the following principal objectives, each of which supports an aspect of the Institute’s mission:

• Create students that are able to advance the state of knowledge and practice in machine learning through innovative research contributions.
• Create students who are able to integrate and apply principles from computing, statistics, optimization, engineering, mathematics and science to innovate, and create machine learning models and apply them to solve important real-world data intensive problems.
• Create students who are able to participate in multidisciplinary teams that include individuals whose primary background is in statistics, optimization, engineering, mathematics and science.
• Provide a high quality education that prepares individuals for careers in industry, government (e.g., national laboratories), and academia, both in terms of knowledge, computational (e.g., software development) skills, and mathematical modeling skills.
• Foster multidisciplinary collaboration among researchers and educators in areas such as computer science, statistics, optimization, engineering, social science, and computational biology.
• Foster economic development in the state of Georgia.
• Advance Georgia Tech’s position of academic leadership by attracting high quality students who would not otherwise apply to Tech for graduate study.

All PhD programs must incorporate a standard set of Requirements for the Doctoral Degree .

The central goal of the PhD program is to train students to perform original, independent research.  The most important part of the curriculum is the successful defense of a PhD Dissertation, which demonstrates this research ability.  The academic requirements are designed in service of this goal.

The curriculum for the PhD in Machine Learning is truly multidisciplinary, containing courses taught in nine schools across three colleges at Georgia Tech: the Schools of Computational Science and Engineering, Computer Science, and Interactive Computing in the College of Computing; the Schools of Aerospace Engineering, Chemical and Biomolecular Engineering, Industrial and Systems Engineering, Electrical and Computer Engineering, and Biomedical Engineering in the College of Engineering; and the School of Mathematics in the College of Science.

Summary of General Requirements for a PhD in Machine Learning

• Core curriculum (4 courses, 12 hours). Machine Learning PhD students will be required to complete courses in four different areas: Mathematical Foundations, Probabilistic and Statistical Methods in Machine Learning, ML Theory and Methods, and Optimization.   
• Area electives (5 courses, 15 hours).
• Responsible Conduct of Research (RCR) (1 course, 1 hour, pass/fail).  Georgia Tech requires that all PhD students complete an RCR requirement that consists of an online component and in-person training. The online component is completed during the student’s first semester enrolled at Georgia Tech.  The in-person training is satisfied by taking PHIL 6000 or their associated academic program’s in-house RCR course.
• Qualifying examination (1 course, 3 hours). This consists of a one-semester independent literature review followed by an oral examination.
• Doctoral minor (2 courses, 6 hours).
• Research Proposal.  The purpose of the proposal is to give the faculty an opportunity to give feedback on the student’s research direction, and to make sure they are developing into able communicators.
• PhD Dissertation.

Almost all of the courses in both the core and elective categories are already taught regularly at Georgia Tech.  However, two core courses (designated in the next section) are being developed specifically for this program.  The proposed outlines for these courses can be found in the Appendix. Students who complete these required courses as part of a master’s program will not need to repeat the courses if they are admitted to the ML PhD program.

Core Courses

Machine Learning PhD students will be required to complete courses in four different areas. With the exception of the Foundations course, each of these area requirements can be satisfied using existing courses from the College of Computing or Schools of ECE, ISyE, and Mathematics.

Machine Learning core:

Mathematical Foundations of Machine Learning. This required course is the gateway into the program, and covers the key subjects from applied mathematics needed for a rigorous graduate program in ML. Particular emphasis will be put on advanced concepts in linear algebra and probabilistic modeling. This course is cross-listed between CS, CSE, ECE, and ISyE.

ECE 7750 / ISYE 7750 / CS 7750 / CSE 7750 Mathematical Foundations of Machine Learning

Probabilistic and Statistical Methods in Machine Learning

• ISYE 6412 , Theoretical Statistics
• ECE 7751 / ISYE 7751 / CS 7751 / CSE 7751 Probabilistic Graphical Models
• MATH 7251 High Dimension Probability
• MATH 7252 High Dimension Statistics

Machine Learning: Theory and Methods.   This course serves as an introduction to the foundational problems, algorithms, and modeling techniques in machine learning.  Each of the courses listed below treats roughly the same material using a mix of applied mathematics and computer science, and each has a different balance between the two. 

• CS 7545 Machine Learning Theory and Methods
• CS 7616 , Pattern Recognition
• CSE 6740 / ISYE 6740 , Computational Data Analysis
• ECE 6254 , Statistical Machine Learning
• ECE 6273 , Methods of Pattern Recognition with Applications to Voice

Optimization.   Optimization plays a crucial role in both developing new machine learning algorithms and analyzing their performance.  The three courses below all provide a rigorous introduction to this topic; each emphasizes different material and provides a unique balance of mathematics and algorithms.

• ECE 8823 , Convex Optimization: Theory, Algorithms, and Applications
• ISYE 6661 , Linear Optimization
• ISYE 6663 , Nonlinear Optimization
• ISYE 7683 , Advanced Nonlinear Programming

After core requirements are satisfied, all courses listed in the core not already taken can be used as (appropriately classified) electives.

In addition to meeting the core area requirements, each student is required to complete five elective courses. These courses are required for getting a complete breadth in ML. These courses must be chosen from at least two of the five subject areas listed below. In addition, students can use up to six special problems research hours to satisfy this requirement. 

i. Statistics and Applied Probability : To build breadth and depth in the areas of statistics and probability as applied to ML.

• AE 6505 , Kalman Filtering
• AE 8803 Gaussian Processes
• BMED 6700 , Biostatistics
• ECE 6558 , Stochastic Systems
• ECE 6601 , Random Processes
• ECE 6605 , Information Theory
• ISYE 6402 , Time Series Analysis
• ISYE 6404 , Nonparametric Data Analysis
• ISYE 6413 , Design and Analysis of Experiments
• ISYE 6414 , Regression Analysis
• ISYE 6416 , Computational Statistics
• ISYE 6420 , Bayesian Statistics
• ISYE 6761 , Stochastic Processes I
• ISYE 6762 , Stochastic Processes II
• ISYE 7400 , Adv Design-Experiments
• ISYE 7401 , Adv Statistical Modeling
• ISYE 7405 , Multivariate Data Analysis
• ISYE 8803 , Statistical and Probabilistic Methods for Data Science
• ISYE 8813 , Special Topics in Data Science
• MATH 6221 , Probability Theory for Scientists and Engineers
• MATH 6266 , Statistical Linear Modeling
• MATH 6267 , Multivariate Statistical Analysis
• MATH 7244 , Stochastic Processes and Stochastic Calculus I
• MATH 7245 , Stochastic Processes and Stochastic Calculus II

ii. Advanced Theory: To build a deeper understanding of foundations of ML.

• AE 8803 , Optimal Transport Theory and Applications
• CS 7280 , Network Science
• CS 7510 , Graph Algorithms
• CS 7520 , Approximation Algorithms
• CS 7530 , Randomized Algorithms
• CS 7535 , Markov Chain Monte Carlo Algorithms
• CS 7540 , Spectral Algorithms
• CS 8803 , Continuous Algorithms
• ECE 6283 , Harmonic Analysis and Signal Processing
• ECE 6555 , Linear Estimation
• ISYE 7682 , Convexity
• MATH 6112 , Advanced Linear Algebra
• MATH 6241 , Probability I
• MATH 6262 , Advanced Statistical Inference
• MATH 6263 , Testing Statistical Hypotheses
• MATH 6580 , Introduction to Hilbert Space
• MATH 7338 , Functional Analysis
• MATH 7586 , Tensor Analysis
• MATH 88XX, Special Topics: High Dimensional Probability and Statistics

iii. Applications: To develop a breadth and depth in variety of applications domains impacted by/with ML.

• AE 6373 , Advanced Design Methods
• AE 8803 , Machine Learning for Control Systems
• AE 8803 , Nonlinear Stochastic Optimal Control
• BMED 6780 , Medical Image Processing
• BMED 6790 / ECE 6790 , Information Processing Models in Neural Systems
• BMED 7610 , Quantitative Neuroscience
• BMED 8813 BHI, Biomedical and Health Informatics
• BMED 8813 MHI, mHealth Informatics
• BMED 8813 MLB, Machine Learning in Biomedicine
• BMED 8823 ALG, OMICS Data and Bioinformatics Algorithms
• CHBE 6745 , Data Analytics for Chemical Engineers
• CHBE 6746 , Data-Driven Process Engineering
• CS 6440 , Introduction to Health Informatics
• CS 6465 , Computational Journalism
• CS 6471 , Computational Social Science
• CS 6474 , Social Computing
• CS 6475 , Computational Photography
• CS 6476 , Computer Vision
• CS 6601 , Artificial Intelligence
• CS 7450 , Information Visualization
• CS 7476 , Advanced Computer Vision
• CS 7630 , Autonomous Robots
• CS 7632 , Game AI
• CS 7636 , Computational Perception
• CS 7643 , Deep Learning
• CS 7646 , Machine Learning for Trading
• CS 7647 , Machine Learning with Limited Supervision
• CS 7650 , Natural Language Processing
• CSE 6141 , Massive Graph Analysis
• CSE 6240 , Web Search and Text Mining
• CSE 6242 , Data and Visual Analytics
• CSE 6301 , Algorithms in Bioinformatics and Computational Biology
• ECE 4580 , Computational Computer Vision
• ECE 6255 , Digital Processing of Speech Signals
• ECE 6258 , Digital Image Processing
• ECE 6260 , Data Compression and Modeling
• ECE 6273 , Methods of Pattern Recognition with Application to Voice
• ECE 6550 , Linear Systems and Controls
• ECE 8813 , Network Security
• ISYE 6421 , Biostatistics
• ISYE 6810 , Systems Monitoring and Prognosis
• ISYE 7201 , Production Systems
• ISYE 7204 , Info Prod & Ser Sys
• ISYE 7203 , Logistics Systems
• ISYE 8813 , Supply Chain Inventory Theory
• HS 6000 , Healthcare Delivery
• MATH 6759 , Stochastic Processes in Finance
• MATH 6783 , Financial Data Analysis

iv. Computing and Optimization: To provide more breadth and foundation in areas of math, optimization and computation for ML.

• AE 6513 , Mathematical Planning and Decision-Making for Autonomy
• AE 8803 , Optimization-Based Learning Control and Games
• CS 6515 , Introduction to Graduate Algorithms
• CS 6550 , Design and Analysis of Algorithms
• CSE 6140 , Computational Science and Engineering Algorithms
• CSE 6643 , Numerical Linear Algebra
• CSE 6644 , Iterative Methods for Systems of Equations
• CSE 6710 , Numerical Methods I
• CSE 6711 , Numerical Methods II
• ECE 6553 , Optimal Control and Optimization
• ISYE 6644 , Simulation
• ISYE 6645 , Monte Carlo Methods
• ISYE 6662 , Discrete Optimization
• ISYE 6664 , Stochastic Optimization
• ISYE 6679 , Computational methods for optimization
• ISYE 7686 , Advanced Combinatorial Optimization
• ISYE 7687 , Advanced Integer Programming

v. Platforms : To provide breadth and depth in computing platforms that support ML and Computation.

• CS 6421 , Temporal, Spatial, and Active Databases
• CS 6430 , Parallel and Distributed Databases
• CS 6290 , High-Performance Computer Architecture
• CSE 6220 , High Performance Computing
• CSE 6230 , High Performance Parallel Computing

Qualifying Examination

The purpose of the Qualifying Examination is to judge the candidate’s potential as an independent researcher.

The Ph.D. qualifying exam consists of a focused literature review that will take place over the course of one semester.  At the beginning of the second semester of their second year, a qualifying committee consisting of three members of the ML faculty will assign, in consultation with the student and the student’s advisor, a course of study consisting of influential papers, books, or other intellectual artifacts relevant to the student’s research interests.  The student’s focus area and current research efforts (and related portfolio) will be considered in defining the course of study.

At the end of the semester, the student will submit a written summary of each artifact which highlights their understanding of the importance (and weaknesses) of the work in question and the relationship of this work to their current research.  Subsequently, the student will have a closed oral exam with the three members of the committee.  The exam will be interactive, with the student and the committee discussing and criticizing each work and posing questions related the students current research to determine the breadth of student’s knowledge in that specific area.  

The success of the examination will be determined by the committee’s qualitative assessment of the student’s understanding of the theory, methods, and ultimate impact of the assigned syllabus.

The student will be given a passing grade for meeting the requirements of the committee in both the written and the oral part. Unsatisfactory performance on either part will require the student to redo the entire qualifying exam in the following semester year. Each student will be allowed only two attempts at the exam.

Students are expected to perform the review by the end of their second year in the program.

Doctoral Dissertation

The primary requirement of the PhD student is to do original and substantial research.  This research is reported for review in the PhD dissertation, and presented at the final defense.  As the first step towards completing a dissertation, the student must prepare and defend a Research Proposal.  The proposal is a document of no more than 20 pages in length that carefully describes the topic of the dissertation, including references to prior work, and any preliminary results to date.  The written proposal is submitted to a committee of three faculty members from the ML PhD program, and is presented in a public seminar shortly thereafter.  The committee members provide feedback on the proposed research directions, comments on the strength of writing and oral presentation skills, and might suggest further courses to solidify the student’s background.  Approval of the Research Proposal by the committee is required at least six months prior to the scheduling of the PhD defense. It is expected that the student complete this proposal requirement no later than their fourth year in the program. The PhD thesis committee consists of five faculty members: the student’s advisor, three additional members from the ML PhD program, and one faculty member external to the ML program.  The committee is charged with approving the written dissertation and administering the final defense.  The defense consists of a public seminar followed by oral examination from the thesis committee.

Doctoral minor (2 courses, 6 hours): 

The minor follows the standard Georgia Tech requirement: 6 hours, preferably outside the student’s home unit, with a GPA in those graduate-level courses of at least 3.0.  The courses for the minor should form a cohesive program of study outside the area of Machine Learning; no ML core or elective courses may be used to fulfill this requirement and must be approved by your thesis advisor and ML Academic Advisor.  Typical programs will consist of three courses two courses from the same school (any school at the Institute) or two courses from the same area of study. 

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PhD in Artificial Intelligence

To enter the Doctor of Philosophy in Artificial Intelligence, you must apply online through the UGA  Graduate School web page . There is an application fee, which must be paid at the time the application is submitted.

There are several items which must be included in the application:

• Standardized test scores, including the GRE. 
• 3 letters of recommendation, preferably from university faculty and/or professional supervisors. We encourage you to submit the letters to the graduate school online as you complete the application process.
• A sample of scholarly writing, in English. This can be anything you've written but should give an accurate indication of your writing abilities. The writing sample can be a term paper, research report, journal article, published paper, college paper, etc.
• A completed  Application for Graduate Assistantship , if you are interested in receiving funding. 
• A Statement of Purpose.
• A Resume or Curriculum Vitae.

Further information on program admissions is found in the AI Institute Frequently Asked Questions (FAQ) . 

International Students should also review the links on the  Information for International Students  page for additional information relevant to the application process.

Graduate School Policies

University of Georgia Graduate School policies and requirements apply in addition to (and, in cases of conflict, take precedence over) those described here. It is essential that graduate students familiarize themselves with Graduate School policies, including minimum and continuous enrollment  and other policies contained in the Graduate School Bulletin.

Students should also familiarize themselves with Graduate School Dates and Deadlines relevant to the degree.

Degree Requirements

Students of the doctoral program must complete a minimum of 40 hours of graduate coursework and 6 hours of dissertation credit (for a total of 46 credit hours), pass a comprehensive examination, and write and defend a dissertation. In addition, the University requires that all first-year graduate students enroll in a 1-credit-hour GradFirst seminar . Each of these requirements is described in greater detail below.

The degree program is offered using an in-person format, and classes are in general scheduled for full-time students. There are currently no special provisions for part-time, online, or off-campus students. Students are expected to attend all meetings of classes for which they are registered.

Program of Study

The Program of Study must include a minimum of 40 hours of graduate course work and a minimum of 6 hours of dissertation credit. Of the 40 hours of graduate course work, at least 20 hours must be 8000-level or 9000-level hours.

Required Courses

The following courses must be completed unless specifically waived for students entering the program with a master’s degree in Artificial Intelligence or a related field, or for students with substantially related graduate course work. All waived credits may be replaced by an equal number of doctoral research or doctoral dissertation credits (ARTI 9000, Doctoral Research or ARTI 9300, Doctoral Dissertation). Substitutions must be approved for a particular student by that student's Advisory Committee and by the Graduate Coordinator.

• PHIL/LING 6510  Deductive Systems (3 hours)
• CSCI 6380  Data Mining (4 hours) or CSCI 8950  Machine Learning (4 hours)
• CSCI/PHIL 6550  Artificial Intelligence (3 hours)
• ARTI 6950  Faculty Research Seminar (1 hour)
• ARTI/PHIL 6340 Ethics and Artificial Intelligence (3 hours)

Elective Courses

In addition to the required courses above, at least 6 additional courses must be taken from Groups A and Group B below, subject to the following requirements. 

• At least 2 courses must be taken from Group A, from at least 2 areas.
• At least 2 courses must be taken from Group B, from at least 2 areas.
• At least 3 courses must be taken from a single area comprising the student’s chosen area of emphasis .

Since not all courses have the same number of credit hours, Ph.D. students may need to take additional graduate courses to complete the 40 hours.

AREA 1: Artificial Intelligence Methodologies

• CSCI 6560  Evolutionary Computing (4 hours)
• CSCI 8050  Knowledge Based Systems (4 hours)
• CSCI/PHIL 8650  Logic and Logic Programming (4 hours)
• CSCI 8920  Decision Making Under Uncertainty (4 hours)
• CSCI/ENGR 8940  Computational Intelligence (4 hours)
• CSCI/ARTI 8950  Machine Learning (4 hours)

AREA 2: Machine Learning and Data Science

• CSCI 6360  Data Science II (4 hours)
• CSCI 8360  Data Science Practicum (4 hours)
• CSCI 8945  Advanced Representation Learning (4 hours)
• CSCI 8955  Advanced Data Analytics (4 hours)
• CSCI 8960  Privacy-Preserving Data Analysis (4 hours)

AREA 3: Machine Vision and Robotics

• CSCI/ARTI 6530  Introduction to Robotics (4 hours)
• CSCI 6800  Human Computer Interaction (4 hours)
• CSCI 6850  Biomedical Image Analysis (4 hours)
• CSCI 8850  Advanced Biomedical Image Analysis (4 hours)
• CSCI 8820  Computer Vision and Pattern Recognition (4 hours)
• CSCI 8530  Advanced Topics in Robotics (4 hours)
• CSCI 8535  Multi Robot Systems (4 hours)

AREA 4: Cognitive Modeling and Logic

• PHIL/LING 6300  Philosophy of Language (3 hours)
• PHIL 6310  Philosophy of Mind (3 hours)
• PHIL/LING 6520  Model Theory (3 hours)
• PHIL 8310  Seminar in Philosophy of Mind (max of 3 hours)
• PHIL 8500  Seminar in Problems of Logic (max of 3 hours)
• PHIL 8600  Seminar in Metaphysics (max of 3 hours)
• PHIL 8610  Epistemology (max of 3 hours)
• PSYC 6100  Cognitive Psychology (3 hours)
• PSYC 8240  Judgment and Decision Making (3 hours)
• CSCI 6860  Computational Neuroscience (4 hours)

AREA 5: Language and Computation

• ENGL 6885  Introduction to Humanities Computing (3 hours)
• LING 6021  Phonetics and Phonology (3 hours)
• LING 6080  Language and Complex Systems (3 hours)
• LING 6570  Natural Language Processing (3 hours)
• LING 8150  Generative Syntax (3 hours)
• LING 8580  Seminar in Computational Linguistics (3 hours)

AREA 6: Artificial Intelligence Applications

• ELEE 6280  Introduction to Robotics Engineering (3 hours)
• ENGL 6826  Style: Language, Genre, Cognition (3 hours)
• ENGL/LING 6885  Introduction to Humanities Computing (3 hours)
• FORS 8450  Advanced Forest Planning and Harvest Scheduling (3 hours)
• INFO 8000  Foundations of Informatics for Research and Practice
• MIST 7770  Business Intelligence (3 hours)

Students may under special circumstances use up to 6 hours from the following list to apply towards the Electives group requirement. 

• ARTI 8800  Directed Readings in Artificial Intelligence
• ARTI 8000  Topics in Artificial Intelligence

Other courses may be substituted for those on the Electives lists, provided the subject matter of the course is sufficiently related to artificial intelligence and consistent with the educational objectives of the Ph.D. degree program. Substitutions can be made only with the permission of the student's Advisory Committee and the Graduate Coordinator.

In addition to the specific PhD program requirements, all first-year UGA graduate students must enroll in a 1 credit-hour GRSC 7001 (GradFIRST) seminar which provides foundational training in research, scholarship, and professional development. Students may enroll in a section offered by any department, but it is recommended that they enroll in a section offered by AI Faculty Fellows for AI students. More information is available at the  Graduate School website .

Core Competency

Core competency must be exhibited by each student and certified by the student’s advisory committee. This takes the form of achievement in the required courses of the curriculum. Students entering the Ph.D. program with a previous graduate degree sufficient to cover this basic knowledge will need to work with their advisory committee to certify their core competency. Students entering the Ph.D. program without sufficient graduate background to certify core competency must take at least three of the required courses, and then pursue certification with their advisory committee. A grade average of at least 3.56 (e.g., A-, A-, B+) must be achieved for three required courses (excluding ARTI 6950). Students below this average may take the fourth required course and achieve a grade average of at least 3.32 (e.g., A-, B+, B+, B).

Core competency is certified by the unanimous approval of the student's Advisory Committee as well as the approval by the Graduate Coordinator. Students are strongly encouraged to meet the core competency requirement within their first three enrolled academic semesters (excluding summer semester).  Core Competency Certification must be completed before approval of the Final Program of Study.

Comprehensive Examination

Each student of the doctoral program must pass a Ph.D. Comprehensive Examination covering the student's advanced coursework. The examination consists of a written part and an oral part. Students have at most two attempts to pass the written part. The oral part may not be attempted unless the written part has been passed.

Admission to Candidacy

The student is responsible for initiating an application for admission to candidacy once all requirements, except the dissertation prospectus and the dissertation, have been completed.

Dissertation and Dissertation Credit Hours

In addition to the coursework and comprehensive examination, every student must conduct research in artificial intelligence under the direction of an advisory committee and report the results of his or her research in a dissertation acceptable to the Graduate School. The dissertation must represent originality in research, independent thinking, scholarly ability, and technical mastery of a field of study. The dissertation must also demonstrate competent style and organization. While working on his/her dissertation, the student must enroll for a minimum of 6 credit hours of ARTI 9300 Doctoral Dissertation spread over at least 2 semesters.

Advisory Committee

Before the end of the third semester, each student admitted into the program should approach relevant faculty members and form an advisory committee. Until the committee is formed, the student will be advised by the graduate coordinator. The committee consists of a major professor and two other faculty members, as follows:

• The major professor and at least one other member must be full members of the Graduate Program Faculty.
• The major professor and at least one other member must be Institute for Artificial Intelligence Faculty Fellows.

Deviations from the 3-member advisory committee structure, including having more members, are in some cases permitted but must conform to Graduate School policies. 

The major professor and advisory committee shall guide the student in planning the dissertation.  The committee shall agree upon, document, and communicate expectations for the dissertation. These expectations may include publication or submission requirements, but, should not exceed reasonable expectations for the given research domain. During the planning stage, the student will prepare a dissertation prospectus in the form of a detailed written dissertation proposal. It should clearly define the problem to be addressed, critique the current state-of-the-art, and explain the contributions to research expected by the dissertation work. When the major professor certifies that the dissertation prospectus is satisfactory, it must be formally considered by the advisory committee in a meeting with the student. This formal consideration may not take the place of the comprehensive oral examination.

Approval of the dissertation prospectus signifies that members of the advisory committee believe that it proposes a satisfactory research study. Approval of the prospectus requires the agreement of the advisory committee with no more than one dissenting vote as evidenced by their signing an appropriate form to be filed with the graduate coordinator’s office.  

Graduation Requirements - Forms and Timeline

Before the end of the third semester in residence, a student must begin submitting to the Graduate School, through the graduate coordinator, the following forms: (i) a Preliminary Program of Study Form and (ii) an Advisory Committee Form. The Program of Study Form indicates how and when degree requirements will be met and must be formulated in consultation with the student's major professor. An Application for Graduation Form must also be submitted directly to the Graduate School. Forms and Timing must be submitted as follows:

• Advisory Committee Form (G130)—end of third semester
• Core Competency Form (Internal to IAI)—beginning of fourth semester
• Preliminary Doctoral Program of Study Form—Fourth semester
• Final Program of Study Form (G138)—before Comprehensive Examination
• Application for Admission to Candidacy (G162)—after Comprehensive Examination
• Application for Graduation Form (on Athena)—beginning of last semester
• Approval Form for Doctoral Dissertation (G164)—last semester
• ETD Submission Approval Form (G129)—last semester

Students should frequently check the Graduate School Dates and Deadlines webpage to ensure that all necessary forms are completed in a timely manner.

Student Handbook

Additional information on degree requirements and AI Institute policies can be found in the AI Student Handbook .

For information regarding the graduate programs in IAI, please contact: 

Evette Dunbar [email protected] Boyd GSRC, Room 516 706-542-0358

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College of Computing

Ph.d. in machine learning, about the curriculum.

The central goal of the Ph.D. program is to train students to perform original, independent research. The most important part of the curriculum is the successful defense of a Ph.D. dissertation, which demonstrates this research ability.

The curriculum is designed with the following principal educational goals:

•    Students will develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline. •    Students will develop a deep understanding and set of skills and expertise in a specific theoretical aspect or application area of the machine learning discipline. •    The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance. •    Students will be able to engage in multidisciplinary activities by being able to communicate complex ideas in their area of expertise to individuals in other fields, be able to understand complex ideas and concepts from other disciplines, and be able to incorporate these concepts into their own work. The curriculum for the Ph.D. in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech:  •    Computer Science (Computing) •    Computational Science and Engineering (Computing) •    Interactive Computing (Computing) – see Computer Science •     Aerospace Engineering (Engineering) •     Biomedical Engineering (Engineering) •     Electrical and Computer Engineering (Engineering) •     Industrial Systems Engineering (Engineering) •     Mathematics (Sciences) Students must complete four core courses, five electives, a qualifying exam, and a doctoral dissertation defense. All doctorate students are advised by ML Ph.D. Program Faculty . All coursework must be completed before the Ph.D. proposal. An overall GPA of 3.0 is required for the Ph.D. coursework.

Research Opportunities

Our faculty comes from all six colleges across Georgia Tech’s campus, creating many interdisciplinary research opportunities for our students. Our labs focus on research areas such as artificial intelligence, data science, computer vision, natural language processing, optimization, machine learning theory, forecasting, robotics, computational biology, fintech, and more.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school. 

The Machine Learning Ph.D. admissions process works bottom-up through the home schools. Admissions decisions are made by the home school, and then submitted to the Machine Learning Faculty Advisory Committee (FAC) for final approval. Support for incoming students (including guarantees of teaching assistantships and/or fellowships) is determined by the home schools. 

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

The machine learning doctorate degree prepares students for a variety of positions in industry, government, and academia. These positions include research, development, product managers, and entrepreneurs. 

Graduates are well prepared for position in industry in areas such as internet companies, robotic and manufacturing companies and financial engineering, to mention a few. Positions in government and with government contractors in software and systems are also possible career paths for program graduates. Graduates are also well-suited for positions in academia involving research and education in departments concerned with the development and application of data-driven models in engineering, the sciences, and computing. 

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

From the Catalog:

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Stanford Online

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Carnegie Mellon University School of Computer Science

Machine learning department.

Ph.D. in Machine Learning

Machine learning is dedicated to furthering scientific understanding of automated learning and to producing the next generation of tools for data analysis and decision-making based on that understanding. The doctoral program in machine learning trains students to become tomorrow's leaders in this rapidly growing area.

Joint Ph.D. in Machine Learning and Public Policy

The Joint Ph.D. Program in Machine Learning and Public Policy is a new program for students to gain the skills necessary to develop state-of-the-art machine learning technologies and apply these technologies to real-world policy issues.

Joint Ph.D. in Neural Computation and Machine Learning

This Ph.D. program trains students in the application of machine learning to neuroscience by combining core elements of the machine learning Ph.D. program and the Ph.D. in neural computation offered by the Center for the Neural Basis of Cognition.

Joint Ph.D. in Statistics and Machine Learning

This joint program prepares students for academic careers in both computer science and statistics departments at top universities. Students in this track will be involved in courses and research from both the Department of Statistics and the Machine Learning Department.

Visit the Website

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Machine Learning - CMU

Joint phd program in autonomous & human decision making.

The goal of this joint PhD program is to train students in both the technology of AI and also the human behavior contexts in which AI systems are used, with specific focus on decision making. Students will be trained in fundamentals of AI, AI-enabled decision making, fundamentals of human decision and behavioral science, cognitive models of decision making, and societal impact of AI technologies.  We anticipate that students will find placements in academic Computer Science or Cognitive Science departments and in non-academic organizations who seek AI and behavioral decision science expertise.

Before applying to the joint degree: MLD students must take and pass 5 courses (listed below) prior to joining the joint program. ( MLD students will typically apply in May of their 2nd year) Three required courses: 10715 Advanced Intro to ML (Fall of first year) 36705 Intermediate Statistics (Fall of first year) 10716 Advanced ML: Theory and Methods (Spring of first year).

Plus two out of the following (from the Social & Decision Sciences (SDS) Department at CMU): 88-702 Behavioral Economics (Spring of first year) 88-703 Human Judgment and Decision Making (Fall of second year) 88-718 Large Scale Social Phenomena (note: not offered every year) Students applying to the joint degree must identify two mentors, one from MLD and one from SDS. Students admitted via MLD or SDS must complete three additional courses: ● 10-718 (ML in practice) ● 10-734 Foundation of autonomous decision making under uncertainty (New course) ● 10-XXX/88-XXX Human-AI Complementarity for Decision Making. These courses could be taken before entry to the program as well.

PhD students are required to be a Teaching Assistant (TA) twice, one TA-ship has to be within MLD and one within SDS. Additionally, students must finish speaking and writing skills requirements. Dissertation Proposal: A PhD thesis will be a contribution to the combination of Machine Learning and Social & Decision Sciences. The proposal must be passed by 48 months (mid August, end of the fourth year). The thesis committee should be assembled by the student and their advisor, and approved by the MLD and SDS PhD Program Director(s). The dissertation committee must contain at least five members, including: ● Two co-chairs, one from MLD and one from SDS. ● At least one external member (usually external to CMU) ● At least one additional MLD Core or Affiliated Faculty member ● At least one additional SDS Regular faculty member The proposal must be passed at least 6 months (typically, much earlier) before the dissertation is defended.

For questions send email to: Tom Mitchell or  Diane Stidle

Social and Decision Sciences Joint Program Requirements SDS Requirements

For questions send email to: John Miller

Students interested in this joint PhD degree should apply to the PhD program that best aligns with their research interests (PhD in Machine Learning or Social & Decision Sciences). Machine Learning PhD online Application

Social & Decision Sciences PhD online Application

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Machine Learning and Big Data PhD Track

Optional PhD Tracks:   Statistical Genetics ,  Statistics in the Social Sciences ,  Machine Learning and Big Data

About The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. Students in this track will have a multidisciplinary experience, taking courses across departments and interacting with faculty and graduate students from these departments. A similar PhD track is being offered in  Computer Science and Engineering  (CSE), and students from both of these tracks will interact significantly in the core courses.

More details about ML @ UW can be found  here  and  here .

For application details, click  here .

Program Requirements

• Statistics Core:  STAT 570 ,  STAT 581 ,  STAT 582
• ML/BD Core:
• (i) Foundational ML:  STAT 535 (ii) One advanced ML course:  STAT 538  or  STAT 548 (iii) One CSE course:  CSE 544  (Databases) or CSE 512 (Visualization) (iv) One MLBD related elective such as a course from the list below and Two electives from the general electives list:        * Advanced Statistical Learning ( STAT 538 )       * Machine Learning for Big Data ( STAT 548 )       * Graphical Models ( CSE 515 )       * Visualization (CSE 512)       * Databases ( CSE 544 )       * Convex Optimization ( EE 578 )
• All other statistics PhD requirements hold, except  STAT 571  may be used to satisfy the Applied Data Analysis Project.
• STAT 583 is not required.

Advanced Data Science Transcriptable Option A student in the MLBD track can, in addition, choose to enroll/satisfy the Advanced Data Science Option. To further expand students' education and create a campus-wide community, students will register for at least 4 quarters in the weekly  eScience Community Seminar . Satisfying this option means that the student will have "ADS" listed on their transcript.

• eScience ADSO

ML Lunch Series A lunchtime seminar on a topic related to machine learning is held nearly weekly on Tuesdays during term. Lunch is provided. Updates are posted  here .

ML Mailing List General announcements related to machine learning are made on the  ML mailing list .

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MastersInAI.org

PhD in Artificial Intelligence Programs

On This Page:

Universities offer a variety of Doctor of Philosophy (Ph.D.) programs related to Artificial Intelligence (AI.) Some of these are titled as Ph.D.s in AI, whereas most are Ph.D.s in Computer Science or related engineering disciplines with a specialization or focus in AI. Admissions requirements usually include a related bachelor’s degree and, sometimes, a master’s degree. Moreover, most Ph.D. programs expect academic excellence and strong recommendations. The AI Ph.D. programs take three to five or more years, depending on if you have a master’s and the complexity of your dissertation. People with Ph.D.s in AI usually go on to tenure track professorships, postdoctoral research positions, or high-level software engineering positions.

What Are Artificial Intelligence Ph.D. Programs?

Ph.D. programs in AI focus on mastering advanced theoretical subjects, such as decision theory, algorithms, optimization, and stochastic processes. Artificial intelligence covers anything where a computer behaves, rationalizes, or learns like a human. Ph.D.s are usually the endpoint to a long educational career. By the time scholars earn Ph.D.s, they have probably been in school for well over 20 years.

People with an AI Ph.D. degree are capable of formulating and executing novel research into the subtopics of AI. Some of the subtopics include:

• Environment adaptation in self-driving vehicles
• Natural language processing in robotics
• Cheating detection in higher education
• Diagnosing and treating diseased in healthcare

AI Ph.D. programs require candidates to focus most of their coursework and research on AI topics. Most culminate in a dissertation of published research. Many AI Ph.D. recipients’ dissertations are published in peer-reviewed journals or presented at industry-leading conferences. They go on to lead careers as experts in AI technology.

Types of Artificial Intelligence Ph.D. Programs

Most AI Ph.D. programs are a Ph.D. in Computer Science with a concentration in AI. These degrees involve general, advanced level computer science courses for the first year or two and then specialize in AI courses and research for the remainder of the curriculum.

AI Ph.D.s offered in other colleges like Computer Engineering, Systems Engineering, Mechanical Engineering, or Electrical Engineering are similar to Ph.D.s in Computer Science. They often involve similar coursework and research. For instance, colleges like Indiana University Bloomington’s Computing and Engineering have departments specializing in AI or Intelligent Engineering. Some colleges, however, may focus more on a specific discipline. For example, a Ph.D. in Mechanical Engineering with an AI focus is more likely to involve electric vehicles than targeted online advertising.

Some AI programs fall under a Computational Linguistics specialization, like CUNY . These programs emphasize the natural language processing aspect of AI. Computational Linguistics programs still involve significant computer science and engineering but also require advanced knowledge in language and speech.

Other unique programs offer a joint Ph.D. with non-engineering disciplines, such as Carnegie Mellon’s Joint Ph.D. in Machine Learning and Public Policy, Statistics, or Neural Computation .

How Ph.D. in Artificial Intelligence Programs Work

Ph.D. programs usually take three to six years to complete. For example, Harvard lays out a three+ year track where the last year(s) is spent completing your research and defending your dissertation. Many Ph.D. programs have a residency requirement where you must take classes on-campus for one to three years. Moreover, most universities, such as Brandeis , require Ph.D. students to grade and/or teach for one to four semesters. Despite these requirements, several Ph.D. programs allow for part-time or full-time students, like Drexel .

Admissions Requirements

Ph.D. programs in AI admit the strongest students. Most applications require a resume, transcripts, letters of recommendation, and a statement of interest. Many programs require a minimum undergraduate GPA of 3.0 or higher, although some allow for statements of explanation if you have a lower GPA due to illness or other excusable causes for a low GPA.

Many universities, like Cornell , recently made the GRE either optional or not required because the GRE provides little prediction into the success of research and represents a COVID-19 risk. These programs may require the GRE again in the future. However, many schools still require the IELTS/TOEFL for international applicants.

Curriculum and Coursework

The curriculum for AI Ph.D.s varies based on the applicants’ prior education for many universities. Some programs allow applicants to receive credit for relevant master’s programs completed prior to admission. The programs require about 30 hours of advanced research and classes. Other programs do not give credit for master’s programs completed elsewhere. These require over 60 hours of electives, in addition to the 30-hours of fundamental and core classes in addition to the advanced courses.

For programs with more specific specialties, the courses are usually narrowly focused. For example, Duke’s Robotics track requires ten classes, at least three of which are focused on AI as it relates to robotics. Others allow for non-AI-specific courses such as computer networks.

Many Ph.D. programs have strict GPA requirements to remain in the program. For example, Northeastern requires PhD candidates to maintain at least a 3.5 GPA. Other programs automatically dismiss students with too many Cs in courses.

Common specializations include:

• Computational Linguistics
• Automotive Systems
• Data Science

Artificial Intelligence Dissertations

Most Ph.D. programs require a dissertation. The dissertation takes at least two years to research and write, usually starting in the second or third year of the Ph.D. curriculum. Moreover, many programs require an oral presentation or defense of the dissertation. Some universities give an award for the best dissertation of the year. For example, Boston University gave a best dissertation award to Hao Chen for the dissertation entitled “ Improving Data Center Efficiency Through Smart Grid Integration and Intelligent Analytics .”

A couple of programs require publications, like Capitol Technology , or additional course electives, like LIU . For example, The Ohio State University requires 27 hours of graded coursework and three hours with an advisor for non-thesis path candidates. Thesis-path candidates only have to take 18 hours of graded coursework but must spend 12 hours with their advisors.

Are There Online Ph.D. in Artificial Intelligence Programs?

Officially, the majority of AI Ph.D. programs are in-person. Only one university, Capitol Technology University , allows for a fully online program. This is one of the most expensive Ph.D.s in the field, costing about $60,000. However, it is also one of the most flexible programs. It allows you to complete your coursework on your own schedule, perhaps even while working. Moreover, it allows for either a dissertation path or a publication path. The coursework is fully focused on AI research and writing, thus eliminating requirements for more general courses like algorithms or networks.

One detail you should consider is that the Capitol Technology Ph.D. program is heavily driven by a faculty mentor. This is someone you will need consistent contact with and open communication. The website only lists the director, so there is a significant element of uncertainty on how the program will work for you. But doctoral candidates who are self-driven and have a solid idea of their research path have a higher likelihood of succeeding.

If you need flexibility in your Ph.D. program, you may find some professors at traditional universities will work with you on how you meet and conduct the research, or you may find an alternative degree program that is online. Although a Ph.D. program may not be officially online, you may be able to spend just a semester or two on campus and then perform the rest of the Ph.D. requirements remotely. This is most likely possible if the university has an online master’s program where you can take classes. For example, the Georgia Institute of Technology does not have a residency requirement, has an online master’s of computer science program , and some professors will work flexibly with doctoral candidates with whom they have a close relationship.

What Jobs Can You Get with a Ph.D. in Artificial Intelligence?

Many Ph.D. graduates work as tenure track professors at universities with AI classes. Others work as postdoc research scientists at universities. Both of these roles are expected to conduct research and publish, but professors have more of an expectation to teach, as well. Universities usually have a small number of these positions available. Moreover, postdoc research positions tend to only last for a limited amount of time.

Other engineers with AI-focused-Ph.D.s conduct research and do software development in the private sector at AI-intensive companies. For example, Google uses AI in many departments. Its assistant uses natural language processing to interface with users through voice. Moreover, Google uses AI to generate news feeds for users. Google, and other industry leaders, have a strong preference for engineers with Ph.D.s. This career path is often highly sought by new Ph.D. recipients.

Another private sector industry shifting to AI is vehicle manufacturing. For example, self-driving cars use significant AI to make ethical and legal decisions while operating. Another example is that electric vehicles use AI techniques to optimize performance and power usage.

Some AI Ph.D. recipients become c-suite executives, such as Chief Technology Officers (CTO). For example, Dr. Ted Gaubert has a Ph.D. in engineering and works as a CTO for an AI-intensive company. Another CTO, Dr. David Talby , revolutionized AI with a new natural language processing library, Spark. CTO positions in AI-focused companies often have decades of experience in the AI field.

How Much Do Ph.D. in Artificial Intelligence Programs Cost?

The tuition for many Ph.D. programs is paid through fellowships, graduate research assistantships, and teaching assistantships. For example, Harvard provides full support for Ph.D. candidates. Some programs mandate teaching or research to attend based on the assumption that Ph.D. candidates need financial assistance.

Fellowships are often reserved for applicants with an exceptional academic and research background. These are usually named for eminent alumni, professors, or other scholars associated with the university. Receiving such a fellowship is a highly respected honor.

For programs that do not provide full assistance, the usual cost is about $500 to $1,000 per credit hour, plus university fees. On the low end, Northern Illinois University charges about $557 per credit hour . With 30 to 60 hours required, this means the programs cost about $30,000 to over $60,000 out of pocket. Typically, Ph.D. programs that do not provide funding for any Ph.D. candidates are less reputable or provide other benefits, such as flexibility, online programs, or fewer requirements.

How Much Does a Ph.D. in AI Make?

Engineers with AI Ph.D.s earn well into the six-figure range in the private sector. For example, OpenAI , a non-profit, pays its top researchers over $400,000 per year. Amazon pays its data scientists with Ph.D.s over $200,000 in salary. Directors and executives with Ph.D.s often earn over $1,000,000 in private industry.

When considering working in the private industry, professionals usually compare offers based on total compensation, not just salary. Many companies offer large stock and bonus packages to Ph.D.-level engineers and scientists.

Startups sometimes pay less in salary, but much more in stock options. For example, the salary may be $50,000 to $100,000, but when the startup goes public, you may end up with hundreds of thousands in stock options. This creates a sense of ownership and investment in the success of the startup.

Computer science professors and postdoctoral researchers earn about $90,000 to $160,000 from universities. However, they increase their competition by writing books, speaking at conferences, and advising companies. Startups often employ professors for advice on the feasibility and design of their technology.

Schools with PhD in Artificial Intelligence Programs

Arizona state university.

School of Computing and Augmented Intelligence

Tempe, Arizona

Ph.D. in Computer Science (Artificial Intelligence Research)

Ph.d. in computing and information sciences (artificial intelligence research), university of california-riverside.

Department of Electrical and Computer Engineering

Riverside, California

Ph.D. in Electrical Engineering - Intelligent Systems Research Area

University of california-san diego.

Electrical and Computer Engineering Department

La Jolla, California

Ph.D. in Intelligent Systems, Robotics and Control

Colorado state university-fort collins.

The Graduate School

Fort Collins, Colorado

Ph.D. in Computer Science - Artificial Intelligence Research Area

University of colorado boulder.

Paul M. Rady Mechanical Engineering

Boulder, Colorado

PhD in Robotics and Systems Design

District of columbia, georgetown university.

Department of Linguistics

Washington, District of Columbia

Doctor of Philosophy (Ph.D.) in Linguistics - Computational Linguistics

The university of west florida.

Department of Intelligent Systems and Robotics

Pensacola, Florida

Ph.D. in Intelligent Systems and Robotics

University of central florida.

Department of Electrical & Computer Engineering

Orlando, Florida

Doctorate in Computer Engineering - Intelligent Systems and Machine Learning

Georgia institute of technology.

Colleges of Computing, Engineering, and Sciences

Atlanta, Georgia

Ph.D. in Machine Learning

Northern illinois university.

Dekalb, Illinois

Ph.D. in Computer Science - Artificial Intelligence Area of Emphasis

Ph.d. in computer science - machine learning area of emphasis, northwestern university.

McCormick School of Engineering

Evanston, Illinois

PhD in Computer Science - Artificial Intelligence and Machine Learning Research Group

Indiana university bloomington.

Department of Intelligent Systems Engineering

Bloomington, Indiana

Ph.D. in Intelligent Systems Engineering

Ph.d. in linguistics - computational linguistics concentration, capitol technology university.

Doctoral Programs Department

Laurel, Maryland

Doctor of Philosophy (PhD) in Artificial Intelligence

Offered Online

Johns Hopkins University

Whiting School of Engineering

Baltimore, Maryland

Doctor of Philosophy in Mechanical Engineering - Robotics

Massachusetts, boston university.

College of Engineering

Boston, Massachusetts

PhD in Computer Engineering - Data Science and Intelligent Systems Research Area

Phd in systems engineering - automation, robotics, and control, brandeis university.

Department of Computer Science

Waltham, Massachusetts

Ph.D. in Computer Science - Computational Linguistics

Harvard university.

School of Engineering and Applied Sciences

Cambridge, Massachusetts

Ph.D. in Applied Mathematics

Northeastern university.

Khoury College of Computer Science

Ph.D. in Computer Science - Artificial Intelligence Area

University of michigan-ann arbor.

Electrical Engineering and Computer Science Department

Ann Arbor, Michigan

PhD in Electrical and Computer Engineering - Robotics

University of nebraska at omaha.

College of Information Science & Technology

Omaha, Nebraska

PhD in Information Technology - Artificial Intelligence Concentration

University of nevada-reno.

Computer Science and Engineering Department

Reno, Nevada

Ph.D. in Computer Science & Engineering - Intelligent and Autonomous Systems Research

Rutgers university.

New Brunswick, New Jersey

Ph.D. in Linguistics with Computational Linguistics Certificate

Stevens institute of technology.

Schaefer School Of Engineering & Science

Hoboken, New Jersey

Ph.D. in Computer Engineering

Ph.d. in electrical engineering - applied artificial intelligence, ph.d. in electrical engineering - robotics and smart systems research, cornell university.

Ithaca, New York

Linguistics Ph.D. - Computational Linguistics

Ph.d.in computer science, cuny graduate school and university center.

New York, New York

Ph.D. in Linguistics - Computational Linguistics

Long island university-brooklyn campus.

Graduate Department

Brooklyn, New York

Dual PharmD/M.S. in Artificial Intelligence

Rochester institute of technology.

Golisano College of Computing and Information Sciences

Rochester, New York

North Carolina

Duke university.

Duke Robotics

Durham, North Carolina

Ph.D in ECE - Robotics Track

Ph.d. in mems - robotics track, ohio state university-main campus.

Department of Mechanical and Aerospace Engineering

Columbus, Ohio

PhD in Mechanical Engineering - Automotive Systems and Mobility (Connected and Automated Vehicles)

University of cincinnati.

College of Engineering and Applied Science

Cincinnati, Ohio

PhD in Computer Science and Engineering - Intelligent Systems Group

Oregon state university.

Corvallis, Oregon

Ph.D. in Artificial Intelligence

Pennsylvania, carnegie mellon university.

Machine Learning Department

Pittsburgh, Pennsylvania

PhD in Machine Learning & Public Policy

Phd in neural computation & machine learning, phd in statistics & machine learning, phd program in machine learning, drexel university.

Philadelphia, Pennsylvania

Doctorate in Mechanical Engineering - Robotics and Autonomy

Temple university.

Computer & Information Sciences Department

PhD in Computer and Information Science - Artificial Intelligence

University of pittsburgh-pittsburgh campus.

School of Computing and Information

Ph.D. in Intelligent Systems

The university of texas at austin.

Austin, Texas

Ph.D. with Graduate Portfolio Program in Robotics

The university of texas at dallas.

Erik Jonsson School of Engineering and Computer Science

Richardson, Texas

University of Utah

Mechanical Engineering Department

Salt Lake City, Utah

Doctor of Philosophy - Robotics Track

University of washington-seattle campus.

Seattle, Washington

Ph.D. in Machine Learning and Big Data

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Best Online Doctorates in Artificial Intelligence: Top PhD Programs, Career Paths, and Salary

The best online PhD in Artificial Intelligence (AI) can get you a high-paying job at a tech company or an exciting position in a research institution. You don’t have to go to college on-campus to earn an online PhD in Artificial Intelligence. All you need is a laptop and a stable Internet connection.

Getting an artificial intelligence PhD online opens the door to many career options and improves your qualifications in the job market. Artificial intelligence jobs are well-paid and will continue to grow in importance in the years to come. Continue reading to learn more about the best PhD programs to help you narrow your school search and begin your doctoral studies.

Find your bootcamp match

Can you get a phd in artificial intelligence online.

Yes, you can get a PhD in Artificial Intelligence online. However, there aren’t many educational programs offering this online degree since AI is most commonly offered as a specialization area in doctoral programs in computer and data science. Additionally, many universities offer PhD degrees only on campus.

There are many online graduate degrees in machine learning, a subset of artificial intelligence. Additionally, there are several doctoral degrees available in big data, a fundamental tool for applied artificial intelligence technologies. You may want to consider including PhD Degrees in Data Science in your school search, as they offer well-structured curricula geared toward AI.

Is an Online PhD Respected?

Yes, an online PhD is respected. It’s a doctoral degree with academic equivalence to in-campus programs. Degree holders can access the same career opportunities and research positions in AI as on-campus graduates.

The respect from employers and research institutions for online PhD programs is mainly related to the reputation of the school, regardless of whether it’s an on-campus or online degree. Be sure to apply only to online PhD degrees from schools with official accreditation and that host relevant artificial intelligence research projects.

What Is the Best Online PhD Program in Artificial Intelligence?

The best online PhD program in artificial intelligence is the Doctor of Science in Computer Science from Aspen University. This entirely online doctoral program offers an excellent education in AI and is flexible and affordable for busy professionals and part-time students.

Why Aspen University Has the Best Online PhD Program in Artificial Intelligence

Aspen University has the best PhD program in artificial intelligence because it allows students to focus on their passion by choosing a capstone project of their interest within the branch of AI. With a total cost of $30,500, the Doctor of Science in Computer Science is the most affordable online doctoral program.

Students enrolled at Aspen University have up to ten years to complete the program at their own pace. Graduates from this program can join research teams in the public or private sectors to find and develop new applications for artificial intelligence and software engineering.

Best Online Master’s Degrees

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Online PhD in Artificial Intelligence Admission Requirements

The admission requirements for an online PhD in Artificial Intelligence vary considerably depending on the institution. For example, Capitol Technology University requires a master’s degree in a relevant field, two letters of recommendation, and a resume that shows five years of related work experience.

On the other hand, the PhD in Computer Science from Northcentral University only requires a master’s degree from an accredited institution. The online Doctor of Science in Computer Science from Aspen University requires applicants to be competent object oriented programming (OOP) developers, or they can take prerequisite courses to fulfill the requirement.

Some universities require GRE exam scores and personal statement essays, and all programs will require official transcripts of previous academic work. Prospective English as a second language (ESL) and international students will need to provide proof of English proficiency in the form of the Test of English as a Foreign Language (TOEFL) exam scores.

• Master’s degree in fields relevant to AI, computer science, or machine learning
• Resume showing relevant work experience
• GRE exam scores, depending on the school
• TOEFL exam or equivalent for ESL and international students
• Letters of recommendation
• Entrance essay or personal statement describing your research interests
• Interview by phone or in-person with a representative of the doctoral program, depending on the school

Best Online PhDs in Artificial Intelligence: Top Degree Program Details

Best Online PhDs in Artificial Intelligence: Top University Programs to Get a PhD in Artificial Intelligence Online

The top university programs to get a PhD in Artificial Intelligence online include Aspen University, Capitol Technology University, Colorado Technical University, Northcentral University, and the University of North Dakota. 

These schools offer the best online AI PhDs, which include PhDs in Artificial Intelligence and Big Data Analysis doctoral programs. Some online educational programs in computer science offer specialization courses in artificial intelligence and machine learning as part of their curricula.

Aspen University is a modern private institution with broad academic programs for distance learning. It has four schools offering online bachelor’s degrees and master’s and doctoral programs. Aspen has an open admission policy, and courses begin every two weeks. The university provides a self-paced approach to studying, and students have between two and ten years to complete any graduate degree.

Doctor of Science in Computer Science

The Doctor of Science in Computer Science program from the School of Business and Technology at Aspen University includes courses in artificial intelligence, the automata complexity theory, and computer ethics. Graduate students of this program will also have the opportunity to work on a research dissertation of their interest. 

Doctor of Science in Computer Science Overview

• Accreditation: Distance Education Accrediting Commission
• Program Length: 2 to 10 years (self-paced)
• Acceptance Rate: 100% (open admission policy)
• Tuition and Fees: $30,500

Doctor of Science in Computer Science Admission Requirements

• Demonstrate professional object oriented programming (OOP) skills
• Master’s degree 
• Official transcripts with a GPA of 3.0 or higher
• Statement of goals, indicating academic, professional, and personal goals

Capitol Technology University is a private university that was originally founded in 1927 to educate radio and electronics technicians. The university is home to the Space Operation Institute, which partners with NASA to train astronautical engineers. The university has just over 740 students in undergraduate and graduate STEM programs.

PhD in Artificial Intelligence

The PhD in Artificial Intelligence degree program aims to engineer computer systems that match the human brain’s decision-making processes. Students conduct unique research with the guidance of skilled academic supervisors. Students can graduate with a thesis or by publishing three articles in high-impact journals. Sixty credit hours are required to earn the degree.

PhD in Artificial Intelligence Overview

• Accreditation: Middle States Commission on Higher Education
• Program Length: 3 years
• Acceptance Rate: N/A
• Tuition: $933/credit

PhD in Artificial Intelligence Admission Requirements

• A master’s degree in a field relevant to artificial intelligence
• Official transcripts
• Online application and non-refundable application fee of $100
• A resume that demonstrates 5 years of related work experience
• Two recommendation forms 
• Personal essay on your technological expertise and how you are prepared to succeed in the program

Colorado Technical University (CTU) is a private university founded in 1965 to help veterans transition into civilian life. CTU is tech-oriented and offers over 80 undergraduate and graduate programs. The university prides itself on enabling US military personnel stationed overseas to take part in CTU’s numerous online learning options.

Doctorate in Big Data Analytics Online

This comprehensive program focuses on the fundamental theoretical, research, and field applications of AI. You will need 100 credit hours to complete this doctoral program , four of which correspond to participation in two in-campus symposia. The cost of the entire program is $64,640.

Doctorate in Big Data Analytics Online Overview

• Accreditation: Higher Learning Commission
• Tuition and Fees: $64,640 total

Doctorate in Big Data Analytics Online Admission Requirements

• Conversation with an admissions advisor to discuss your academic goals
• Interview by phone or in-person with a department representative
• Entrance essay
• Accredited bachelor’s degree

Northcentral University is an accredited, private online school founded in 1996 and based in San Diego, California. All of NCU’s programs are offered online, and the school has over 10,000 online students. The school does not require GRE, GMAT, or any standardized test scores for admission, and the doctoral faculty offers one-to-one career counseling.

PhD in Computer Science 

You’ll need 60 credit hours to complete the PhD in Computer Science program online at NCU. The total estimated cost of the program is $68,560 and can be completed in 3 years. The curriculum is designed for graduate students to explore theoretical concepts and gain relevant skills in their preferred areas of expertise, including AI, data mining , and cyber security. 

PhD in Computer Science Overview

• Tuition and Fees: $68,560 total or $3,282/credit

PhD in Computer Science Admission Requirements

• Master’s degree from a regionally or nationally accredited academic institution
• Online application

The University of North Dakota (UND) is a public research university that was founded in 1883 and enrolls over 13,000 graduate and undergraduate students. UND is a leading school in aviation, space, and unmanned aircraft education and offers more than 140 graduate degree programs. 

Students enrolled in the PhD in Computer Science program can take specialization courses in AI and machine learning. The comprehensive program requires 90 credits and explores topics such as bioinformatics, compiler design, software engineering , and the practical and theoretical frameworks of computer science. 

• Program Length: 4 to 5 years
• Tuition and Fees: $869/credit
• Master's Degree in Science or Engineering with an overall minimum GPA of 3.0
• Expertise in high-level programming languages and basic knowledge of data structures
• Basic skills in calculus, statistics, and linear algebra
• Online application and non-refundable application fee of $35
• Three letters of recommendation
• Statement of goals

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Online Artificial Intelligence PhD Graduation Rates: How Hard Is It to Complete an Online PhD Program in Artificial Intelligence?

It can be quite hard to complete an online PhD program in artificial intelligence. According to a study conducted by researchers from Nova Southeastern University, doctoral programs have average attrition rates of over 50 percent and up to 70 percent for online doctoral programs . PhD coursework can be demanding and highly time-consuming for nearly all programs.

Working closely with your academic advisor increases your chances of success and prevents disengagement, especially in online degree programs. An academic advisor will offer career counseling to help you tackle defeatist attitudes. To ensure completion, you should stay in communication with your advisor to make a plan with achievable milestones.

How Long Does It Take to Get a PhD in Artificial Intelligence Online?

It takes about three to five years to get a PhD in Artificial Intelligence online. Some programs take more than five years to complete the credit requirements and the doctoral thesis. Most online programs offer a flexible timeline to complete the degree requirements and are designed for people who are holding a job while they study.

Most programs offer one-to-one career counseling from the doctoral faculty to find solutions for students to allocate time and design strategies to complete the program. Students can pause their studies for set periods of time and resume their studies at a more convenient time for them.

How Hard Is an Online Doctorate in Artificial Intelligence?

An online doctorate in artificial intelligence can be quite hard. It requires good learning skills to complete three to four years of coursework. There is also a considerable financial commitment and long hours of doctoral-level readings and thesis work.

Not all the online PhD programs in artificial intelligence require a high-level education in computer science theory, math, or engineering. Some programs welcome students from any field because nearly every industry is finding new practical applications for AI.

Best PhD Programs

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What Courses Are in an Online Artificial Intelligence PhD Program?

The common courses in an online artificial intelligence PhD program include artificial intelligence research backgrounds, artificial intelligence research methodologies, and artificial intelligence future trends . Some programs also include machine learning, deep learning, and computer vision courses.

Main Areas of Study in an Artificial Intelligence PhD Program

• Machine learning
• Deep learning
• Machine vision
• Natural language processing

How Much Does Getting an Online Artificial Intelligence PhD Cost?

It costs $19,314 on average to get a PhD in Artificial Intelligence, according to the National Center for Education Statistics. The exact costs of the program vary according to the institution. However, this is an average figure, and tuition costs will vary significantly.

Some schools, like the University of North Dakota, allow you to transfer up to 30 credits from approved master’s degree programs. Private schools are typically more expensive than public universities, and some schools will charge more for out-of-state tuition. There are many private and public grants available for students in STEM doctoral programs.

How to Pay for an Online PhD Program in Artificial Intelligence

You can pay for an online PhD in Artificial Intelligence by getting scholarships, grants, loans, and employment or internship opportunities. The University of North Dakota offers positions with remote capabilities that don’t require an on-campus presence. You can also apply for a Direct PLUS Loan from the US Department of Education.

The Federal Student Aid office also offers several types of financial aid for students in graduate programs. Use the Federal Student Aid Estimator to learn how much financial aid you can get from the federal government.

How to Get an Online PhD for Free

You can’t get an online PhD for free. Some online colleges, like Northcentral University, offer a few full-tuition scholarships every semester for online master’s degrees and doctoral programs. The US military also offers full and partial scholarships for service members, their spouses, and children. It’s best to research the financial support options offered by the school.

What Is the Most Affordable Online PhD in Artificial Intelligence Degree Program?

The most affordable online PhD in Artificial Intelligence degree program is the Doctor of Science in Computer Science from Aspen University. The total cost for this doctoral degree is $30,500, consisting of $27,000 in tuition and $3,500 in fees.

Most Affordable Online PhD Programs in Artificial Intelligence: In Brief

Why You Should Get an Online PhD in Artificial Intelligence

You should get an online PhD in Artificial Intelligence because it is a great way to access better-paid positions and improve your relevance in the job market. With a PhD in AI, you can join skilled teams at companies that are developing new AI applications for every industry or choose to teach and do research in academia.

Top Reasons for Getting a PhD in Artificial Intelligence

• Job opportunities. With a PhD in Artificial Intelligence, you have greater opportunities in the job market. Many companies are creating new positions to integrate AI into their products and services. The demand for specialists in artificial intelligence is increasing, particularly for machine learning analysts and programmers.
• Higher salaries. Employees with expertise in AI are very valuable for employers, regardless of the type of industry. A doctoral degree in artificial intelligence shows that you have the highest level of education in AI and are qualified for positions that pay higher salaries.
• Research opportunities. A doctoral degree in artificial intelligence opens doors to postdoctoral programs and internships in the best AI research centers in the US. There is an increasing demand for AI researchers in public and private institutions, and the research skills gained in a PhD program will highly qualify you for these roles.
• New skills. During your academic journey in a doctoral program in artificial intelligence, you will develop technical skills that will be essential in the emerging AI workforce. These skills include the management of large data sets, natural language processing , and machine learning to improve process precision.

Best Master’s Degree Programs

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What Is the Difference Between an On-Campus Artificial Intelligence PhD and an Online PhD in Artificial Intelligence?

The difference between an on-campus artificial intelligence PhD and an online PhD in Artificial Intelligence is how the schools deliver the courses and the way you interact with faculty. The cost of online PhD programs in artificial intelligence is usually the same as on-campus programs. Online students don’t need to commute or pay campus-related fees.

On-campus programs enforce in-person learning, and students work with faculty and other students on campus to do relevant coursework and research. In an online program, all the relevant learning materials, like digital books, research articles, interactive activities, virtual labs, and discussion forums, are located on the school’s online learning platform.

Online PhD vs On-Campus PhD: Key Differences

• Convenience. With an online PhD program, you don’t need to waste time commuting. You can study from the comfort of your home at the time of your convenience. All the materials, including digital books, lectures, and readings, are located on the online learning platform.
• Affordability. Tuition costs are typically the same for both online and on-campus programs. However, with an online PhD program, you don’t have to pay on-campus related fees, like access to athletic facilities or parking.
• Flexible timelines. Most online PhD programs are designed for students who also hold jobs. Faculty and academic advisors will help you create a program timeline that doesn’t overwhelm you. Additionally, online PhD programs allow you to pause your studies and resume them according to your time availability.
• Location. All you need to complete an online PhD program is a computer and an Internet connection. You can bring your laptop to your workplace or complete your assignments while traveling the world. Many students of online PhD programs are military personnel stationed overseas.

How to Get a PhD in Artificial Intelligence Online: A Step-by-Step Guide

To get a PhD in Artificial Intelligence online, you’ll need to meet the degree requirements of your doctoral program. Your academic advisor can provide career counseling to create a strategy according to your time availability. You will earn your PhD degree after completing all the required credit hours and defending your dissertation.

The first step to obtaining your PhD degree is to prepare your documentation for the application process. This includes official transcripts, a resume, and the contact information of your references. Let your references know the admissions office will write or call them to validate their recommendation and prepare yourself for an interview with the program’s coordinator.

The program faculty will assign you an academic advisor who will provide you with academic counseling during your learning journey. You should work closely with them to set realistic and achievable benchmarks for your coursework. 

Your academic advisor will let you know when it's time to choose an area of specialization. You will then select the elective courses according to that specialization. Choose a specialization area according to your job interests after graduation. 

Most online PhD programs in artificial intelligence require you to complete between 60 and 100 credits. Some of the final credits correspond to your dissertation research. You will have to complete the coursework and write a doctoral dissertation with an original contribution.

The last step to earning your PhD in Artificial Intelligence is to present your final oral examination or dissertation defense. If your research represents an original contribution, and the school approves your publishable dissertation, the PhD degree will be yours.

Online PhD in Artificial Intelligence Salary and Job Outlook

The salary and job outlook for professionals with a doctoral degree in artificial intelligence are higher than average, with most salaries above $100,000 per year. There are several well-paid jobs in industries that are increasing the number of AI projects to develop new products and services.

What Can You Do With an Online Doctorate in Artificial Intelligence? 

With an online doctorate in artificial intelligence, you can get a job in AI , a rapidly growing field with new applications in nearly all industries. Tech and software companies include AI components in their programs and online services to gain a competitive advantage over traditional services.

There are many ways to learn artificial intelligence , but a doctoral degree makes you an expert in AI. With an online doctorate in artificial intelligence, you can apply for internships in AI-leading companies and participate in their algorithm development.

Best Jobs with a PhD in Artificial Intelligence

• Artificial Intelligence Specialist
• Machine Learning Engineer
• Research Engineer
• Technical Architect
• Data Scientist

Potential Careers With an Artificial Intelligence Degree

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What Is the Average Salary for an Online PhD Holder in Artificial Intelligence?

The average salary for someone with a PhD in Artificial Intelligence is $115,000 per year , according to PayScale. A machine learning engineer has an average annual salary of $112,709 , while a lecturer will earn an average yearly salary of $77,910 . Continue reading below for some of the top-paying jobs available in the field after graduation.

Highest-Paying Artificial Intelligence Jobs for PhD Grads

Best Artificial Intelligence Jobs for Online PhD Holders

The best artificial intelligence jobs for PhD holders include AI specialist and machine learning engineer. Those that work in this field usually deploy AI models to improve the accuracy and performance of information processes. These jobs require the use of deep learning, machine learning, and natural language processing methods.

Artificial intelligence specialists apply AI principles to find solutions to real-world problems that traditional computing approaches can’t solve. They work in interdisciplinary teams to apply AI components that improve the overall system performance. 

• Salary with an artificial intelligence PhD: $132,995
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, and Washington

Machine learning engineers create and improve engines for processing vast amounts of data, also known as big data. By improving how machine learning models integrate additional sets of data, they increase the predictive capacities of an engine, for example.

• Salary with an artificial intelligence PhD: $124,800

Technical architects with AI expertise design computer networks that follow an AI-centered architecture. They incorporate cloud services into corporate networks, thereby increasing security and reducing costs associated with on-premises computing systems. They also design migration strategies to AI-based computer networks.

• Salary with an artificial intelligence PhD: $118,808
• Job Outlook: 5% job growth from 2020 to 2030
• Number of Jobs: 165,200
• Highest-Paying States: New Jersey, Rhode Island, Delaware, Virginia, and Maryland

Data scientists develop models for data management and processing. They use deep learning techniques to understand customers better and improve products and services with that insight. Algorithm development is one of the most important daily tasks of data scientists.

• Salary with an artificial intelligence PhD: $111,250
• Number of Jobs: 63,200
• Highest-Paying States: Washington, California, Delaware, New York, and New Jersey

Software developers implement artificial intelligent components into programs and online services. These AI components require software development for natural language processing, which is used for voice recognition. Software developers also code programs that use big data to provide accurate search results.

• Salary with an artificial intelligence PhD: $110,140
• Number of Jobs: 1,847,900
• Highest-Paying States: California, Washington, Maryland, New York, and Rhode Island

Is It Worth It to Do a PhD in Artificial Intelligence Online?

Yes, it is worth it to pursue a PhD in Artificial Intelligence online. While a doctoral degree program entails an important investment of time and money, the outcome is worth the effort. A PhD substantially improves your chances of increasing your income with a broader career potential.

Artificial intelligence is a fast-growing field for which all industries are finding new applications every day. An online PhD in Artificial Intelligence shows that you are an expert in AI and that you are qualified to apply for jobs that use artificial intelligence .

Additional Reading About Artificial Intelligence

[query_class_embed] https://careerkarma.com/blog/artificial-intelligence/ https://careerkarma.com/blog/best-artificial-intelligence-bachelors-degrees/ https://careerkarma.com/blog/best-artificial-intelligence-masters-degrees/

Online PhD in Artificial Intelligence FAQ

Anyone who wants to start a career in AI or machine learning can study artificial intelligence. Jobs for artificial intelligence specialists are well-paid and in high demand. You can earn an online degree in artificial intelligence or take an artificial intelligence bootcamp .

Today, artificial intelligence is used in most software-as-a-service (SAAS) applications. It is used in systems that create predictions using big data. AI applications are also found in voice recognition applications, navigation systems, and chatbot assistants.

Artificial intelligence matters because it is increasingly used to improve processes across all domains. Scientists use AI to find new drugs and create new materials. Companies use deep learning to understand their customers better and provide more accurate solutions.

Yes, artificial intelligence will create new jobs. Artificial intelligence engines are increasing their number of cognitive functions to solve problems faster than humans. Meanwhile, there is a growing demand for specialists to find new applications for AI.

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Doctor of Philosophy (PhD) in Artificial Intelligence

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Earn a doctorate degree in Artificial Intelligence, help lead innovation in a growing industry

The PhD in Artificial Intelligence is centered upon how computers operate to match the human decision making process in the brain. Your research will be led by AI experts with both research and industrial expertise. This emerging subject is starting to attract attention on the wider issues as the IOT and other advanced computer systems work in our lives.

This is a research based doctorate PhD degree where you will be assigned an academic supervisor almost immediately to guide you through your program and is based on mostly independent study through the entire program. It typically takes a minimum of two years but typically three years to complete if a student works closely with their assigned academic advisor. Under the guidance of your academic supervisor, you will conduct unique research in your chosen field before submitting a Thesis or being published in three academic journals agreed to by the academic supervisor.  If by publication route it will require original contribution to knowledge or understanding in the field you are investigating.

As your PhD progresses, you move through a series of progression points and review stages by your academic supervisor. This ensures that you are engaged in a process of research that will lead to the production of a high-quality Thesis and/or publications and that you are on track to complete this in the time available. Following submission of your PhD Thesis or accepted three academic journal articles, you have an oral presentation assessed by an external expert in your field.

Why Capitol?

Learn around your busy schedule

Program is 100% online, with no on-campus classes or residencies required, allowing you the flexibility needed to balance your studies and career.

Proven academic excellence

Study at a university that specializes in industry-focused education in technology fields, with a faculty that includes many industrial and academic experts.

Expert guidance in doctoral research

Capitol’s doctoral programs are supervised by faculty with extensive experience in chairing doctoral dissertations and mentoring students as they launch their academic careers. You’ll receive the guidance you need to successfully complete your doctoral research project and build credentials in the field. 

Key Faculty

Dissertation Chair

Degree Details

This program may be completed with a minimum of 60 credit hours, but may require additional credit hours, depending on the time required to complete the dissertation/publication research. Students who are not prepared to defend after completion of the 60 credits will be required to enroll in RSC-899, a one-credit, eight-week continuation course. Students are required to be continuously enrolled/registered in the RSC-899 course until they successfully complete their dissertation defense/exegesis.

The student will produce, present, and defend a doctoral dissertation after receiving the required approvals from the student’s Committee and the PhD Review Boards.

Prior Achieved Credits May Be Accepted

Doctor of Philosophy - 60 credits

Program Objectives:

• Students will integrate and synthesize alternate, divergent, or contradictory perspectives or ideas fully within the field of Artificial Intelligence.
• Students will demonstrate advance knowledge and competencies in Artificial Intelligence.
• Students will analyze existing theories to draw data-supported consultations in Artificial Intelligence.
• Students will analyze theories, tools, and frameworks used in Artificial Intelligence.
• Students will execute a plan to complete a significant piece of scholarly work in Artificial Intelligence.
• Students will evaluate the legal, social, economic, environmental, and ethical impact of actions within Artificial Intelligence and demonstrate advance skill in integrating the results in to the leadership decision-making process.

Learning Outcomes:

Upon graduation, graduates will:

• integrate the theoretical basis and practical applications on Artificial Intelligence in to their professional work;
• demonstrate the highest mastery of Artificial Intelligence;
• evaluate complex problems, synthesize divergent/alternative/contradictory perspectives and ideas fully, and develop advanced solutions to Artificial Intelligence challenges; and
• contribute to the body of knowledge in the study of Artificial Intelligence.

Tuition & Fees

Tuition rates are subject to change.

The following rates are in effect for the 2024-2025 academic year, beginning in Fall 2024 and continuing through Summer 2025:

• The application fee is $100
• The per-credit charge for doctorate courses is $950. This is the same for in-state and out-of-state students.
• Retired military receive a $50 per credit hour tuition discount
• Active duty military receive a $100 per credit hour tuition discount for doctorate level coursework.
• Information technology fee $40 per credit hour.
• High School and Community College full-time faculty and full-time staff receive a 20% discount on tuition for doctoral programs.

Find additional information for 2024-2025 doctorate tuition and fees.

When I was comparing multiple universities, Capitol Tech's admissions staff was responsive, helpful, and caring. This was valuable my deciding factor in choosing Capitol Tech.

-Dr. Jason Collins-Baker PhD in Artificial Intelligence

I chose Capitol for its great reputation in the field I am interested in, as well as its flexibility offering a fully Online program that works very well with my work and family commitments. Another reason was its European PhD program, with which I am familiar since I grew up and studied in Europe (Spain), although I currently live in the United States (California).

-Hector Garcia Villa PhD in Artificial Intelligence

Need more info, or ready to apply?

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[D] Is it possible to get into an ML PhD program without papers these days?

Sorry, if you've seen a similar question before somewhere. I'm a FAANG ML engineer. I only have a Masters in CS (no thesis) and one third author paper in Robotics (from Bachelors). Didn't end up publishing in Masters due to various reasons. Also, didn't do PhD (kept thinking over whether I'd be accepted or not and didn't apply). I've been trying to get into ML research. I want to work on original ideas and not just implement known stuff. I'm trying to transfer internally to some research role but finding it very difficult. Even research engineer roles seem to ask for first-author papers or something (or maybe it's the recession or maybe I don't have the right connections). Keep thinking about if I should press the PhD application button but get demoralized due to my poor research experience. Just wanted to put my dilemma to rest by asking this group.

Debunking 5 artificial intelligence myths

Friday, May 17, 2024

Carlson School Graduate Programs

Since the emergence of ChatGPT, artificial intelligence’s (AI) transformative and disruptive potential has come to dominate news headlines and conversations everywhere. We checked in with our resident AI scholar, Ravi Bapna , to discuss misconceptions and facts surrounding this emerging technology and its related use in business analytics. 

New to business analytics? Get a primer on its historical and future evolution as you learn about what business analytics is and why it’s important. 

Here are five common ideas about artificial intelligence. Are they myth or fact-based?

1. Artificial intelligence is ChatGPT.

One prevalent misconception is equating artificial intelligence with specific AI models like ChatGPT. While ChatGPT is indeed an impressive large language model developed by OpenAI, it represents just one facet of the broader field of artificial intelligence. AI encompasses a wide range of technologies and applications, including traditional machine learning involving prediction and description, casual analytics, natural language processing, and robotics among others.

(For an article about AI, we decided to have a little fun and use AI to help us answer some questions. So this answer was provided by ChatGPT and fact-checked by human subject matter experts.)

2. AI, machine learning, and deep learning are all the same thing.

Another myth is the conflation of artificial intelligence, machine learning, and deep learning. While these terms are related, they refer to distinct concepts within the field of AI.

What is artificial intelligence?

Artificial intelligence is a general term referring to the discipline of creating machines or programs that use data to solve problems or make decisions like humans.

We’ve all been using AI technology for some time now. Here are some you might be familiar with:

• Web search engines like Google Search
• Algorithm-based recommendation systems like Netflix, TikTok, Spotify, and YouTube
• Human speech assistants like Siri and Alexa

What is machine learning?

Machine learning is a way to use AI. It lets a computer learn without providing explicit instructions on how. Machine learning can use data to:

• Explain what happened (descriptive)
• Predict what will happen (predictive)
• Suggest an action (prescriptive)

This method usually requires human monitoring and intervention. For example, humans are needed to manually label datasets used for training, and to write the machine learning algorithms to process the datasets.

Streaming services, like Spotify and Netflix, use machine learning to learn about your preferences and make personalized recommendations. Each time you indicate you like a song or movie, you give the computer more data to make better inferences about what to recommend to you.

What is deep learning (and neural networks)?

Before we get to deep learning, we have to discuss neural networks. Neural networks is a specific category of machine learning algorithms. It’s modeled on how the human brain uses neurons to process rich sensory data (images, video, voice recording, etc.) and come to conclusions. Neural networks are made of node layers: an input layer, one or more hidden layers, and an output layer. Neural networks made of more than three layers of nodes is considered deep learning.

Deep learning lets machine learning algorithms learn and improve on their own. This decreases the need for human monitoring and intervention. For example, humans don’t need to explicitly define input predictors or features of a predictive model, rather the algorithms can work with the raw data. Reducing the amount of human labor needed would make it possible to scale machine learning.

Although we haven’t seen scaled use of the tool yet, you’re probably more familiar with it than you realize. Deep learning models are also the basis for things like Gmail’s Smart Complete and generative AI algorithms like ChatGPT.

3. Artificial intelligence systems are “black boxes” or impossible to understand.

Myth. 

As the Carlson School’s professor Ravi Bapna likes to say, “Everyone is welcome in the House of AI.”

Developed by Bapna and his colleague Anindya Ghose, the House of AI is a framework and visual representation of the different dimensions that make up this technology. It can help us more easily understand what AI is, how it works, and how individuals and societies can use it to solve problems.

On the bottom floor is the foundation of all AI—data engineering. This is where data is cleansed, aggregated, integrated, and transformed for analysis. Next come four pillars of AI that make up how the data can be used:

• Descriptive analysis — answers “What patterns exist?”
• Predictive analysis — answers “What will happen next?”
• Causal — answers “Did x truly cause y?”
• Prescriptive — answers “How should we respond?”

These pillars support more advanced AI concepts like deep learning, reinforcement learning, and generative AI. Finally, over all the technical aspects of AI are practices and values organizations and society must adopt to implement ethical, equitable, explainable, and fair AI.

While AI systems seem complex, they can be understood. Books for a general audience like Professor Ravi Bapna’s forthcoming Thrive: Maximizing Well-Being in an Age of AI , can be great a starting point. If you’re interested in pursuing AI-related careers, in which you’d work closely with these tools, there are many training and degree programs like the Carlson School’s business analytics master’s program .

4. Artificial intelligence will make human labor obsolete.

It depends.

One of the most pervasive myths surrounding artificial intelligence is the fear that it will replace human workers entirely. While AI has the potential to automate certain tasks and processes, it is unlikely to make human labor obsolete. Instead, AI is more accurately viewed as a tool that can augment human capabilities, enhancing productivity and efficiency in various industries. By automating repetitive tasks, AI allows humans to focus on higher-level activities that require creativity, critical thinking, and emotional intelligence.

(The above portion of this answer was written by ChatGPT. We thought it was a decent response but want to further supplement with thoughts from human subject matter experts.)

At the time of this writing, AI is only good at completing one small task at a time. Most of us are working on multiple tasks that also require judgment. While AI tools can eliminate certain tasks and processes, they can’t automate all aspects of our human labor—especially when it comes to innovation.

For example, analytics requires the parsing of data sources and complex datasets to draw insights. AI-powered tools can aid this process, but human analysts are still needed for things like:

• Data quality checks
• Algorithm bias mitigation
• Insight presentations to stakeholders

5. Leveraging AI for business use requires leaders that understand AI and an AI-ready workforce.

According to Professor Bapna, implementing AI to create business value requires “clarity around why AI needs to be used, what AI’s use cases are, and how it can augment human capabilities.” Using AI to its full business potential requires leaders who can envision possibilities, managers who can define opportunities for AI-powered solutions, and data professionals who can use the technology.

And these demands are already here. Based on insights gathered from employer surveys and the Carlson School’s business analytics program advisory board and alumni, there’s a growing need for employees who are proficient in training, implementing, and managing AI models. With the global AI market size expected to have an annual growth rate of 40.2 percent from 2021 to 2028, artificial intelligence literacy will likely be a key differentiator for future analytics professionals and leaders.

How the Carlson School’s business analytics program can help you gain AI skills

In the Carlson School’s Master of Science in Business Analytics (MSBA) program, you’ll develop a good understanding of AI technologies and the ability to harness them responsibly and effectively to create business value.

You do not need any AI experience before the program. In one year, we’ll help you build the foundation you need to understand and deploy advanced AI techniques. You’ll also get hands-on experience training AI models with faculty oversight and support.

We offer an Artificial Intelligence in Business track if you’re interested in enhancing your skills and knowledge in this domain. No matter how much you choose to focus on AI when you’re in our MSBA program, you’ll gain valuable analytics skills and business acumen that will enhance your career prospects.

MSBA Program Snapshot

MSBA Employment Statistics

Browse the MSBA employment report

of 2023 graduates accepted employment offers within 6 months after graduation

Mean base salary for the Class of 2023 graduates

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Complete this form to receive information about the Master of Science in Business Analytics program's coursework, admissions, tuition, and more.

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Machine Learning & Data Science Foundations

Online Graduate Certificate

Apply to Expand Your Future

As the value of data continues to skyrocket, companies are in need of people who can transform large data sets into rich analytical insights. Now, you can learn these techniques in Carnegie Mellon’s cutting-edge online program. Apply today to expand your future in machine learning and data science.  

Are we the right fit?  

Let’s face it, pursuing any kind of advanced training is an investment of your time, energy and resources. Before you consider our program, make sure your background aligns with our program expectations.  

Successful applicants will have:

• A bachelor’s degree in STEM or related field  Successful applicants will hold a degree in a science, technology, engineering or math-related field. Other degrees will be considered if the applicant can show the necessary proficiency in math and programming.  
• Proficiency in advanced math  Students should provide evidence of successful completion of advanced math coursework such as calculus, linear algebra and statistics.  
• Proficiency in programming  Students should be proficient in Python, R, or an analogous programming language, with experience writing at least 1000 lines of code.  
• Relevant work experience   Ideally, applicants will have some relevant work experience in either computer programming or a related field. Internships or other related work are acceptable.  
• A disciplined and motivated mindset  Harder to measure, but equally important, successful applicants will have a resilient spirit, a hunger to learn, and a knack for solving problems through technical innovation. With courses taught by CMU faculty from the #4 computer science school in the country, a consistent and conscious effort will be required to master each topic.

If you have questions about the program or how it aligns with your background, please call 412-501-2686 or send an email to  [email protected]  with your inquiries .

Application Requirements

Ready to apply? Here’s what you’ll need to complete the admissions process: 

✔ Complete the online application Submit your application in the application portal.

✔ Submit your resume/CV We’d like to learn more about your employment history, academic background, technical skills, and professional achievements. Submit a 1 to 2 page resume or CV showcasing your experience. 

✔ Submit your transcripts Submit an unofficial copy of your transcript for each school you attended. Transcripts must include your name, the name of the college or university, the degree awarded (along with the conferral date), as well as the grade earned for each course. Email your transcripts directly to [email protected] .  

✔  Upload a statement of purpose Tell us your professional story. Where have you been, and where do you hope to go? In 500 words or less, please share how our program would advance your capabilities in your current role or prepare you for a new role in the industry. 

✔ Submit your TOEFL, IELTS, or DuoLingo test scores An official TOEFL, IELTS, or DuoLingo test is required for non-native English speakers. This requirement will be waived, however, for applicants who either completed an in-residence bachelor’s, master’s, or doctoral degree program in the United Kingdom, United States, or Canada (excluding Quebec) or have at least three years of professional work experience using English as their primary language. If you fall into one of these categories, please include this information on your resume.  

Tuition: Invest in Your Future

By enrolling in our graduate-level program, you'll be investing in your professional growth to expand your skillset or advance your career. We know this is a significant investment. Not just for you, but for your family as well.

Scholarships To help offset the cost of tuition, and to make our program as accessible as possible, we offer a limited number of partial, merit-based scholarships. All applications will be evaluated for these awards automatically; there is no need to submit additional materials. If you are awarded a scholarship, you will be notified in your decision letter.  All applicants who submit by the priority deadline will receive a partial scholarship award.

In addition, Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in Machine Learning & Data Science Foundations worth up to 20% of tuition. Indicate your alumni status within the application to be eligible.

So, what is the investment per course? Below is a breakdown of our tuition for the 2024/2025 academic year:

• An additional technology fee of approximately $230 will be assessed each semester.
• The rates above are for the 2024/2025 academic year only. If the program is not completed within that time frame, tuition may increase slightly for the following academic year.

Financing Your CMU Graduate Certificate

Monthly payment plan.

CMU provides a monthly payment option , managed by Nelnet Campus Commerce, designed to help students spread out tuition payments into manageable monthly installments. This plan also offers the ease of online enrollment. Should you be admitted and choose to join us, we recommend registering for this plan early to fully benefit from the range of payment options available.

Financial Aid & Private Loans

Students pursuing a graduate certificate are not eligible to receive federal financial aid. However, private loans are a viable alternative to consider with competitive interest rates and borrower benefits. See FastChoice , a free loan comparison service to easily research options.

Employer Tuition Reimbursement

Many companies offer tuition reimbursement programs to foster professional development among their employees. We encourage you to contact your HR department to find out if similar opportunities exist at your workplace. 

When you speak to your employer, you can share that our program: 

• Consists of transcripted, credit-bearing courses (not just continuing education units). You will earn 36 Carnegie Mellon graduate-level credits when you complete the full program.  
• Equips you with foundational skills in AI, machine learning, and computational data science, which means you’ll be ready to extract meaningful insights from large, complex data sets right from the get-go. With the #1 program in Artificial Intelligence and the #1 Programming Languages school in the country, CMU is the ideal place to learn these skills and techniques.
• Features coursework taught by CMU faculty experts who are spearheading research in language technologies, computer science, machine learning, and human-computer interaction. 
• Is delivered completely online , which means you can take classes on your own time while maintaining your normal work schedule.

Not sure how to approach your employer? Need specific documents to proceed with enrollment?  Call 412-501-2686 or send an email to  [email protected]  with your inquiries .  We’re here to help you take the next step in your professional  journey. 

CMU EMPLOYEE TUITION REIMBURSEMENT

The Graduate Certificate in Machine Learning & Data Science Foundations is eligible for CMU tuition remission. Review the   CMU tuition remission policy   to check your eligibility.

A Note for International Applicants

As part of a global university with locations and students from around the world, the School of Computer Science welcomes the diverse perspectives that international students bring to our programs.

The Graduate Certificate in Machine Learning & Data Science Foundations provides a unique opportunity for individuals nearly everywhere to earn a certificate at the intersection of AI, machine learning, and computational data science from one of the top ranked computer science schools in the country. 

To help ensure you are fully prepared for the admissions process and, if admitted, for success as a student, this section provides detailed information about requirements for international applicants.

We look forward to reviewing your application.

The Graduate Certificate in Machine Learning & Data Science Foundations considers for admission international applicants who reside within, or outside of, the domestic United States. International applicants who reside within or outside of the domestic United States are advised of the following information and additional requirements for international applicants to the program.

Student Visas

Since this program is fully online, enrollment in this program will not qualify students for any type of visa to enter or remain in the United States for any purpose. 

Time and Attendance Requirement  

Classes for the program will be taught on the U.S. Eastern Time zone schedule, and students must be available to attend all live classes, regardless of location.

U.S. Sanctions; U.S. Sanctioned Countries

Individuals who are the target of U.S. sanctions or who are ordinarily resident in a U.S. sanctioned country or who live or expect to live in a U.S. sanctioned country while participating in the program are not eligible for admission to this program due to legal restrictions/prohibitions and should not apply. U.S sanctioned countries are currently Belarus, Cuba, Iran, North Korea, Russia, Syria and the following regions of Ukraine: Crimea, Donetsk and Luhansk. In addition, all or a portion of this program may not be available to individuals who are ordinarily resident of certain countries due to legal restrictions.  

Applications received from these individuals will not be accepted. As well, if an individual is admitted to the program and subsequently the individual becomes the target of U.S. sanctions, ordinarily resident of a U.S. sanctioned country or lives in a U.S. sanctioned country while participating in the program (or otherwise becomes ordinarily resident of country in which the program is not available due to legal restrictions), the individual’s continued enrollment in the program may be terminated and/or restricted (due to U.S. legal restrictions/prohibitions) and the individual may not be able to complete the program.  

Licensure in Various Jurisdictions

From time to time Carnegie Mellon reviews the licensing requirements of various jurisdictions in order to assess whether Carnegie Mellon may be precluded from making the program available to applicants that are residents of one or more of these jurisdictions prior to Carnegie Mellon obtaining the relevant license(s). Affected applicants from these jurisdictions, if any, will be notified prior to enrollment if Carnegie Mellon determines that it is unable to make the program available to them for this reason.

Value Added Tax (VAT) and Other Taxes

The tuition, required fees and other amounts quoted for this program do not include charges for applicable Taxes (hereinafter defined). The student is responsible for payment of all applicable Taxes (if any) relating to the tuition, required fees and other amounts required to be paid to Carnegie Mellon for the program, including any Taxes payable as a result of the student’s payment of such Taxes. 

Further, the student must timely make all payments due to Carnegie Mellon without deduction for Taxes, unless the deduction is required by law. If the student is required under applicable law to withhold Taxes from any payment due to Carnegie Mellon, the student is responsible for timely (i) paying to Carnegie Mellon such additional amounts as are necessary so that Carnegie Mellon receives the full amount that it would have received absent such withholding, and (ii) providing to Carnegie Mellon all documentation, if any, necessary to permit the student and/or Carnegie Mellon to claim the application of available tax treaty benefits (for Carnegie Mellon review and completion, if warranted and acceptable). 

Taxes mean any taxes, governmental charges, duties, or similar additions or deductions of any kind, including all use, income, goods and services, value added, excise and withholding taxes assessed by or payable in the student’s country of residence and/or country of payment (but does not include any U.S. federal, state or local taxes).

• What kind of academic background do I need? Successful applicants will have a bachelor’s degree in a STEM-related field. Other degrees will be considered if the applicant can show the necessary proficiency in math and programming. Applicants should also have proficiency in programming languages like Python or R, with experience writing up to 1000 lines of code. 
• Do I need work experience? Applicants will ideally have some relevant work experience in either computer programming or a related field. Internships or other related work are also acceptable.
• What materials do I need to submit when I apply to this program? Besides the online application, applicants must submit a current resume, transcripts, and a personal statement to be considered for enrollment.
• Is there an application fee? No, this program does not require an application fee.
• When is the application deadline?  All applicants who submit by the priority deadline of July 9, 2024 will receive a partial scholarship award. The final deadline to apply is July 30, 2024.
• How do I check the status of my application? You can view the status of your application at any time in the application portal. A decision letter from Carnegie Mellon will be sent through the application portal within a few weeks of submitting your online application.
• After I submit my application, when will I hear back? You’ll receive a decision letter within a few weeks of submitting your application.
• Is a deposit required to secure my spot? No, a deposit is not required to secure your spot in the program.
• If I choose to complete the entire certificate, what is my total investment? The total investment for the Machine Learning & Data Science Foundations certificate during the 2024/2025 academic year is $25,452. A breakdown of the tuition and fees can be found above. Partial scholarships are available. All applicants who submit by the priority deadline of July 9, 2024 will receive a partial scholarship award. Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in Machine Learning & Data Science Foundations worth up to 20% of tuition.
• Is this program eligible for CMU tuition remision? Yes, the Graduate Certificate in Machine Learning & Data Science Foundations is eligible for CMU tuition remission. Review the   CMU tuition remission policy   to check your eligibility.

Application Deadlines

Priority*: July 9, 2024 Final: July 30, 2024

*All applicants who submit by the priority deadline will receive a partial scholarship award.

Request Info

Questions? There are two ways to contact us. Call 412-501-2686 or send an email to  [email protected] with your inquiries.

Fast Admission Decisions

Applications are evaluated on a bi-weekly basis, which means you’ll receive a decision letter fast,  within a few weeks  of submitting your application .  

At CMU, we recognize the value of time well spent. Quick decisions mean less time wasted and more time preparing for your future.

Due to the individual nature of the coursework, space is limited for our program - applications will be accepted until the class is full.