Edited By Christine Skopec
Review & Contribution By Ryan Adams
Explore the Ph.D. in Machine Learning program: advance your expertise, conduct research, and unlock diverse career opportunities in this dynamic field.
Machine Learning is a domain of artificial intelligence that allows software applications to predict different outcomes without being independently programmed accurately. A Ph.D. in machine learning helps individuals understand the most efficient ways to use vast amounts of stored data and conduct analysis and evaluation. This program is designed for those who are interested in conducting advanced research in the domain of machine learning. The Ph.D. in machine learning is generally a comprehensive and research-intensive degree that helps individuals further their education in machine learning. It typically involves students completing vast amounts of coursework, oral examinations, and qualifying exams. Individuals can expect several roles as lead data scientists, machine learning engineers, research scientists, etc.
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National University, headquartered in San Diego, California, was founded in 1971 to provide educational opportunities for adult learners and working professionals. It offers over 190 degree programs, including bachelor’s, master’s, and doctoral degrees, with flexible online and on-campus courses. Serving around 40,000 students and over 240,000 alumni, the university is recognized for its support of U.S. military members. Accredited by the WASC Senior College and University Commission (WSCUC), National University is committed to accessible and transformative higher education.
Find the Best Ph.D. in Machine Learning Programs
While selecting an online doctorate program in machine learning, students would have to narrow down their options judiciously, as earning a Ph.D. could take up a substantial amount of time and resources of the individuals. Therefore, they need to consider factors like the duration of the program, energy resources required to complete the curriculum, availability of suitable guides, accredited programs, and scholarships available to complete the program. The teams at FBD evaluated numerous colleges from all over the U.S. using a proprietary methodology and prepared a list of those offering the best Ph.D. programs in machine learning:
Universities and programs are ranked by various factors, such as affordability, curriculum and coursework, reputation and availability, program length, statistics, the potential of employment, and return on investment for the students. For a more in-depth analysis, please read about our rankings methodology page.
What to Expect from a Ph.D. in Machine Learning Program
A Ph.D. in machine learning program demands rigorous academic dedication and typically spans 4-5 years when pursued full-time. It typically requires an average of 60-75 credits. In addition to coursework, students must pass preliminary and qualifying exams and ultimately complete a dissertation. Teaching and research responsibilities are also part of the program’s requirements. Doctoral candidates collaborate closely with faculty members to develop a robust and independent dissertation. Many institutions offering Ph.D. programs in machine learning primarily offer on-campus learning. Throughout their thesis work, candidates are encouraged to engage in research aimed at addressing real-world challenges pertinent to various industries. Upon graduation, machine learning Ph.D. holders often secure positions in both private and governmental organizations, as well as in research laboratories.
Program Structure, Coursework, and Dissertation
The curriculum of the Ph.D. in machine learning is comprehensive and multidisciplinary and contains courses from other programs like computational science, data science, computer and electrical engineering, and much more. The central goal of this doctoral degree is to train students to perform their independent and original research. The most integral part of the coursework is the successful defense of the dissertation, demonstrating students’ research ability. Students may need to complete an average of 21 credit hours of elective courses, a preliminary exam, a qualifying exam, and a dissertation. Some of the basic components of the Ph.D. machine learning curriculum would be:
- Coursework
Students’ course selection while pursuing a machine learning program is varied. The coursework will also align with the candidate’s professional objectives, which will also help them in the dissertation. Some core curriculum machine learning Ph.D. candidates must include fundamental areas like mathematical foundation, theory and methods, optimization, and probabilistic and statistical methods in machine learning. - Residency
As part of the doctorate machine learning online requirements, students must complete certain residency hours before completing the comprehensive exams. Those who have opted for graduate assistantships may be exempted from this requirement. - Teaching
Teaching is not compulsory for the Ph.D. in machine learning online program. However, in most colleges, teaching experience is recommended for students who aren’t doing any graduate teaching assistantship in collaboration with their institution. It is also a valuable option for students pursuing an academic career. - Comprehensive Exams
Students who complete their coursework become eligible to apply for comprehensive examinations. They are usually required to pick a topic of their choice and write a manuscript on that topic. They must also defend their manuscripts by making oral presentations and presenting them to a committee. The examination could also include a practical and theory exam to ensure students are at a professional level of expertise. - Field Experience
Students are usually encouraged to do independent study and research coursework to integrate and combine their research and classroom theory with varied field activities. They can engage with professionals and collect research data from various sources. - Dissertation
Ph.D. students must work with their advisors to choose topics that will help advance their careers. They often start working on the dissertation after passing the comprehensive exam and completing the curriculum. A student’s dissertation topic will also provide a framework for the curriculum.
Here are examples of research topics in machine learning:
Bayesian Networks
This research focuses on Bayesian Networks, exploring their application in probabilistic modeling and inference. Topics include algorithmic advancements, efficient learning techniques, and practical implementations for decision-making in complex systems. The study aims to enhance predictive accuracy and uncertainty quantification across various domains.
Deep Learning for Combinatorial Problems
This research investigates deep learning techniques tailored to combinatorial problems. Studies concentrate on developing neural network architectures capable of solving complex optimization tasks efficiently. The focus is on advancing algorithms to address scheduling, routing, and graph theory challenges, aiming for practical applicability and scalability.
Mathematical Analysis
This research delves into mathematical analysis to enhance understanding and optimization of machine learning algorithms. Topics encompass theoretical foundations, convergence proofs, and complexity analyses. The study aims to provide insights into algorithm behavior, aiding in developing more robust and efficient learning systems.
Data Analytics and Mining
This research focuses on data analytics and mining, emphasizing the development of novel techniques for extracting actionable insights from large datasets. Topics include data preprocessing, pattern recognition, and predictive modeling. The study aims to advance knowledge discovery and decision-making methodologies across diverse domains.
A few other examples are:
- Data Access And Management
- Divisive Covers
- Automatic Hyperparameter Selection for ISO Map
- Neural Network Verification
- Binarized Neural Networks
- Integrative Data Science
- Machine Learning Algorithms
- Big Data Analytics in Medical Services
- Multitask Variational Autoencoders
- Cytometry Analysis
- Detecting Small Clusters
- Online Learning in Real-Time Systems
- Developing Stress Granule Classifier
- Business Intelligence and Case Studies
Read the Ph.D. Programs Guide to understand more about doctoral programs.
Learning Outcomes and Skills Gained
Completing a doctoral machine-learning program equips students with the skills to construct innovative machine-learning environments for scientific and societal advancements. They gain expertise in fundamental research, including machine learning, artificial intelligence, and data science. This prepares them for leadership roles in data-driven sectors and promising careers in both industry and academia. A Ph.D. student gains several skills from the machine learning Ph.D., including the ability to:
- demonstrate leadership skills in machine learning research and exhibit utmost professionalism in careers in industry or academia;
- demonstrate in-depth knowledge of advances in data availability and computational speed and develop methods of novel data analysis;
- ethical and responsible skills as a trained machine learning professional to develop methods and statistical tools in finding answers to questions in varied academic disciplines of machine learning;
- communicate how to extract, analyze, and interpret critical questions from heterogeneous, big, and uncertain data;
- effectively translate insights of fundamental research into practice in the machine learning field and integrate the results in fields like medicine, industry, science, and governmental programs;
- actively participate in machine learning organizations and assist individuals working in different domains requiring insights from machine learning;
- carry out research that is novel and original to be able to publish it in esteemed scientific and technical journals.
Areas of Specialization for Machine Learning Ph.D. Students
In academic specialization, individuals focus on specific subjects to gain expertise. Companies often prefer candidates with specialized skills over generalized degrees. Students in fields like data science and artificial intelligence opt for machine learning specialization to acquire sought-after skills. Many specializations may not be found under the Ph.D. programs in machine learning as this subject of machine learning is chosen as a concentration.
Full-Time and Part-Time Machine Learning Ph.D. Programs
The average time it would take for students to finish machine learning Ph.D. programs is between 5 to 7 years. They can complete it in an accelerated format within four years, while others could take up to 6 years if the degree is not completed full-time. Ph.D. candidates must undertake detailed coursework and research work regardless of the timeframe they are completing the Ph.D. Here is a quick overview of the timeline for machine learning Ph.D. programs in the U.S.:
- Minimum time required for full-time Ph.D. in machine learning: 4 years average
- Maximum time required for Ph.D. in machine learning: up to 7 years
- Typical time frame to complete a Ph.D. in machine learning: 5 to 7 years.
Accelerated Machine Learning Ph.D. Programs
Accelerated programs at the Ph.D. level will help students complete their degrees in less time than the usual 5-7 years. By completing a Ph.D. in machine learning degree in an accelerated format, students can complete their research and doctoral coursework in four years or less. However, finishing the dissertation and fieldwork takes an additional year to finish coursework, depending on the student. Students could also begin their Ph.D. coursework while completing their master’s program. A Master’s thesis, which an individual completes, can also be upgraded and continued during the research work of a Ph.D. The number of credits they complete during their coursework could also determine the length of the program. Some Ph.D. programs can be regarded as accelerated programs when candidates complete the number of credits divided between coursework and dissertation faster than the given time.
Requirements for Getting into a Ph.D. in Machine Learning Program
There are different requirements to get into the machine learning program. Students who have completed their master’s degree in machine learning are preferred for candidature to Ph.D. However, some programs don’t make a master’s degree mandatory to apply to Ph.D. programs. Since this field is versatile, those with diverse profiles are encouraged to apply to the program. Some of the common admission requirements for a Ph.D. in machine learning are:
- Online application form and fee
- Academic transcripts from universities attended before
- Statement of purpose
- Letters of recommendation (usually three are required)
- Scores of standardized tests like GRE scores
- TOEFL or IELTS scores for non-native English speakers
- Curriculum Vitae or Resume (highlighting relevant experience in data science)
- Writing sample (required for some programs)
No GRE Ph.D. in Machine Learning Programs
The Graduate Record Examination or GRE scores assess if a student can complete their coursework during the timeframe of graduation or Ph.D. independently. Different institutions across the United States do not ask for a GRE score as a minimum requirement at the time of admission. However, some colleges waive the requirement of the GRE or make it an optional requirement where applicants can apply their discretion on submitting their scores.
Some colleges offering Ph.D. in machine learning no GRE programs include:
- Carnegie Mellon University’s[1]– the doctoral program in machine learning has made GRE scores optional. Students must explain the purpose statement if they don’t submit their score.
- Georgia Tech’s[2]– the doctoral program in machine Learning doesn’t require a GRE score for admission.
How to Ensure a Ph.D. in Machine Learning Program Is Accredited
Accreditation is a process that ensures that institutions and programs meet up to the standard quality set up by these regulating bodies. The students can also trust colleges providing such accredited programs. They can also receive expert knowledge and skills from accredited institutions. Most institutions offering machine learning Ph.D. programs are accredited by one of six regional accrediting agencies, which are:
- New England Commission of Higher Education (NECHE)
- Middle States Commission on Higher Education (MSCHE)
- Higher Learning Commission (HLC)
- Southern Association of Colleges and Schools Commission on Colleges (SACSCOC)
- Northwest Commission on Colleges and Universities (NWCCU)
- Western Association of Schools and Colleges (WASC) Senior College and University Commission (WSCUC)
Students can also check if the programs they are interested in are being recognized by the Database of Accredited Postsecondary Institutions and Programs[3] and the Council for Higher Education Accreditation[4].
Read the Accreditation Guide to understand more about accreditation.
Where to Find Free Online Courses for Machine Learning Ph.D. Students
There are many free online platforms where Ph.D. students of machine learning can access courses. Free online courses provide students with accessible resources to enhance their expertise. These courses cover various topics, from basic algorithms to advanced applications, empowering students to supplement their formal education, deepen their understanding, and stay updated with the latest developments in the field, all at no cost. Here are some free machine learning courses:
| Course | Provided by | Description |
|---|---|---|
IBM Machine Learning Professional Certificate[5] | Coursera | This course from IBM can help anyone who wants to develop skills and expertise to pursue careers in machine learning and learn various main types of machine learning, such as reinforcement learning, deep learning, and supervised and unsupervised learning. It consists of 6 courses that provide theoretical understanding and practice of the primary usage of algorithms. |
Data Science: Machine Learning[6] | Harvard University | The free course helps individuals learn about machine learning fundamentals and popular machine learning algorithms. Individuals can also build recommendation systems and understand regularization and its usefulness. Students can distinguish how machine learning differs from other computer-guided decision processes in building prediction algorithms using data. |
Machine Learning[7] | edX | Through the course, individuals learn about supervised classification and regression techniques. Some of the methods individuals understand are linear and logistic regression, tree classifiers, boosting, maximum likelihood, MAP inference, hidden Markov models, Kalman filters, and more. |
Ways to Pay for a Ph.D. in Machine Learning Program
Ph.D. programs could be very beneficial for students in advancing their careers. However, the cost associated with these programs tends to rise every year. Due to the high cost of tuition fees and other additional expenses, candidates might be hesitant to pursue such a degree. They may engage in research projects or work part-time or full-time to finance their education. There are several funding options that those pursuing Ph.D. programs in machine learning can use to fund their education; some are:
Scholarships
Scholarships are funding that students can use and avail themselves of based on their academic merits or other fields like dance, music, sport, and more. They are provided scholarships by their institutions, private organizations, or other non-governmental organizations.
Graduate Assistantships
Students can take advantage of graduate assistantships to fund their education. It is a great opportunity to build excellent contacts and expertise in their field of study. They can also take up graduate assistant roles in institutions, supporting teaching and playing other roles.
Grants
Students are awarded grants not based on their academic merit. Unlike scholarships, students can be awarded grants based on need or merits in other fields like sports, music, and more. Grants can provide this funding in a particular field, like research in machine learning.
Private student loans
Students can take out personal loans from various banking institutions and federal governments. Many Donors and Grants help those who are unable to pay off loans.
FAFSA
FAFSA, or Free Application for Student Aid, is a kind of application that helps determine if candidates can be eligible for federal financial aid. Candidates can determine if they will receive institutional funds based on the application.
Prospective students should read the financial aid guide to understand other funding options and how to pay for college.
Fully Funded Ph.D. in Machine Learning
While some institutions in the United States offer fully funded Ph.D. programs, they are rarely available specifically for machine learning. Pursuing these funded programs can significantly alleviate the considerable expenses associated with a Ph.D. However, fully funded options exist in related fields such as computer science, artificial intelligence, and data science, often including machine learning components. Carnegie Mellon University[8] is an example of a university that offers a fully funded Ph.D. in machine learning.
Career Opportunities and Salaries for Ph.D. in Machine Learning Students After Graduating
Graduating with a Ph.D. in machine learning opens doors to diverse career opportunities with competitive salaries. Professionals with this advanced degree are sought after across various industries, offering roles that demand expertise in data analysis, modeling, and algorithm development. Salaries for Ph.D. graduates in machine learning are often lucrative, reflecting the high demand for their specialized skills in the job market. Listed below are some career opportunities available for Ph.D. in machine learning graduates:
- AI Engineer
- Data Scientist
- Business Intelligence Developer
- Data Mining and Analysis
- Machine Learning Researcher
- Machine Learning Engineer
A few other possible occupations are:
| Occupation | Skills Required | Median Annual Salary | Job Growth (up to 2031) | Job Description |
|---|---|---|---|---|
Machine Learning Engineer[9] | Technical skills, statistical skills, written communication, and analytical skills. | $117,338 | No Data Available | A machine learning engineer is fluent in various software systems and processes, such as APIs, JAVA, C, and SIRI. They use different natural language processing and algorithm designs and work with statistics, frameworks, and big data. |
Computer and Information Research Scientists[10] | Analytical, communication, detail-oriented, interpersonal, logical, math, and problem-solving skills. | $136,620 | 23% (much faster than average) | Computer and information research scientists innovate new uses for technology, tackling intricate computing challenges across various sectors. They collaborate with experts, design software and hardware, develop computing languages, conduct experiments using data science and machine learning techniques, analyze results, and publish findings. Their work enhances computer systems and drives advancements in technology. |
Information Security Analysts[11] | Analytical, communication, creative, detail-oriented, and problem-solving skills. | $112,000 | 32% (much faster than average) | Information security analysts safeguard organizational computer networks and systems by planning and executing security measures. They monitor networks for breaches, maintain security software, identify vulnerabilities, research IT security trends, and develop security standards. They also assist with disaster recovery plans, ensuring the continuity of IT operations during emergencies through preventive measures and restoration strategies. |
Data Scientists [12] | Analytical skills, technical skills, and statistical skills | $103,500 | 35% (much faster than average) | Data scientists use analytical tools to derive insights from data, collecting, categorizing, and analyzing datasets. They develop and validate algorithms and models, visualize findings, and provide business recommendations. Initiating projects by gathering relevant data from various sources, they often work with large, unstructured datasets, structuring them for analysis through cleaning processes. |
Certifications and Licensing for Ph.D. in Machine Learning Graduates
Professional certifications are a standardized process that will let the individual demonstrate their qualification and quality to perform in a particular field. By earning professional certification, students can increase their chances of employability and prove that they hold the highest professional standards. Those graduating with a machine learning Ph.D. program could obtain various professional certifications, such as:
Professional ML Engineer Certification-Google Cloud [13]
The Professional Machine Learning Engineer Certification by Google Cloud equips individuals to build, evaluate, and optimize ML models using Google Cloud technologies. They handle complex datasets, prioritize responsible AI, and collaborate cross-functionally. Proficient in programming and MLOps, they design scalable ML solutions, enabling organizational teams to leverage machine learning effectively.
AWS Certified Machine Learning – Specialty Certification[14]
The AWS Certified Machine Learning – Specialty Certification validates expertise in building, training, tuning, and deploying machine learning models on AWS. Designed for individuals with over a year of experience in ML/deep learning workloads on AWS, it signals proficiency in cloud-based ML initiatives and best practices, enhancing organizational talent development.
Data Scientist-Advanced Analytics[15]
The Data Scientist-Advanced Analytics certification teaches about practical aspects that enable students to participate in big data analytics projects effectively. Individuals also learn about data scientists’ role in analyzing and exploring data-building statistics and evaluating theories and methods of advanced data analytics.
Generally, graduates don’t need a license to work as a machine learning professional. Licensing in other fields of machine learning, like data science, artificial intelligence, etc., in the United States could depend on factors like level of expertise, employer, education, and state where they work.
Additional Resources for Ph.D. in Machine Learning Graduates
Membership in organizations specializing in machine learning can greatly benefit Ph.D. students in the field. These groups offer scholarships, mentorship opportunities, and access to industry experts, aiding career advancement and providing practical insights. Interacting with professionals in similar domains enhances understanding and fosters collaboration, helping students refine their research focus and navigate the complexities of the field effectively. Here are a few additional resources machine learning doctoral program students can explore:
| Title/Resource | Description |
|---|---|
Association for the Advancement of Artificial Intelligence | The Association for the Advancement of Artificial Intelligence promotes the responsible usage of artificial intelligence, increases the public’s understanding of artificial intelligence, and provides guidance for research planners and funders regarding potential and current AI developments and their future implications. |
American Statistical Association | The American Statistical Association has more than 16,000 members serving in academic, governmental, and industrial agencies in the U.S. and abroad. They provide expertise in different areas, such as examining social issues, environmental risks, and therapeutic drug development. |
Data Science Council of America | The Data Science Council of America is an association in which data science professionals develop the next generation of adaptable data talent and can develop very advanced big data technologies. They also focus on certifications that will help professionals work as experts. |
Institute for Operations Research and Management Science | The Institute for Operations Research and the Management Sciences (INFORMS) is a professional society dedicated to advancing operations research and analytics. It provides a platform for researchers, practitioners, and educators to collaborate, share knowledge, and promote the application of quantitative methods to solve complex problems in various industries worldwide. |
The National Artificial Intelligence Initiative | The National Artificial Intelligence Initiative (NAII) is a comprehensive strategy by the United States government to accelerate research, development, and deployment of artificial intelligence technologies. It aims to foster collaboration between government agencies, academia, and industry to ensure U.S. leadership in AI innovation while addressing ethical and societal implications. |
FAQs About Ph.D. in Machine Learning Programs
Why should you do an online Ph.D. program in machine learning?
Online Ph.D. programs in machine learning offer flexibility for working professionals, enabling them to balance academic pursuits with career commitments. They provide access to top-tier faculty, diverse research opportunities, and collaborative networks without geographic constraints. Additionally, online formats often offer asynchronous learning, accommodating various schedules and enhancing accessibility.
Can you do a Ph.D. program in machine learning online?
What are the most popular funding options for a machine learning program?
Is a Ph.D. in machine learning worth it?
Is a Ph.D. in machine learning hard?
What can you do with a Ph.D. in machine learning?
Is a Ph.D. in machine learning different from a Ph.D. in artificial intelligence?
Citations:
Database of Accredited Postsecondary Institutions and Programs
Council for Higher Education Accreditation
IBM Machine Learning Professional Certificate- Coursera
Data Science: Machine Learning- Harvard
Payscale- Machine Learning Engineer
U.S. Bureau of Labor Statistics: Occupational Outlook for Information Security Analysts
U.S. Bureau of Labor Statistics: Occupational Outlook for Data Scientists
Professional ML Engineer Certification-Google Cloud
Disclaimer:
The average tuition (based on degree type for in-state students), average graduation rates, and rankings are based on data from various sources, including the Integrated Postsecondary Education Data System (IPEDS), and are variable over time. All rankings and statistics are subject to change. The rankings are solely the opinion of Find Best Degrees (FBD) and are based on our proprietary methodology. They do not represent the views of the institutions or organizations mentioned, nor do they represent any official government census or survey. Furthermore, any views or opinions expressed on this page are of FBD’s researchers and teams. Unless otherwise specified, they do not represent the thoughts and opinions of the individuals, institutions, or organizations mentioned. This page’s content is provided solely for informational purposes, with data drawn from various sources, including IPEDS. FBD and its employees make no guarantees regarding the accuracy or completeness of any information found on this page or by following any link. FBD will not be held liable for any errors or omissions in this material nor any losses, injuries, or damages resulting from the exposure or use of this information. Although the information on this page is/was correct at the time of publication, readers should exercise caution because some or all of the provided information may have changed over time, potentially resulting in inaccuracies. For more information, please read our Terms of Service. Trademarks and logos are the property of their registered owners.