Pushing the envelope of research and development and building the most complex systems of logic and reasoning humankind has ever built is a challenge not even the best master’s graduate is geared for. When it is time to think the deep thoughts, to come up with the innovative breakthrough, and to architect the systems of the future, PhD graduates are the ones that industry and academia turn to.
A PhD in machine learning can put you in that exclusive group. Not only does that education give you the tools to pursue amazing new things in AI, but it can also hand you the keys to success in half a dozen other important industries. Machine learning has become the foundation of fields like:
- Data Science
- Computer Vision
- Natural Language Processing/Speech Recognition
- Fraud Detection
Machine learning also plays an important part in fields ranging from statistical physics to neurological research.
While these are some of the hardest educational credentials you can get in any field, they unlock a world of possibilities when it comes to machine learning.
A PhD in Machine Learning Goes Deep on a Foundational Technique in AI Studies
Today’s headlines don’t spend a lot of time drawing a line between artificial intelligence and machine learning. But when you get to the point in your career where you’re considering a doctorate, you had better have a clear understanding of how machine learning is unique in the broader field of artificial intelligence.
While machine learning and artificial intelligence are closely related, they aren’t the same thing. It becomes very clear when engaging with it at the doctoral level how machine learning stands out as a highly specialized tool within the overall world of AI technologies.
A PhD in machine learning can turn you into a specialist at the leading edge of new developments in artificial intelligence today.
But ML today is among the most important of those technologies in the overall development and advancement of AI capabilities. Cutting-edge applications like generative tools for text and imagery, deep learning tools predicting biological and chemical structures, and automated language translation are coming from machine learning.
At the same time, many PhDs in ML don’t necessarily spend a lot of time on AI applications. Parallel uses in data science, mathematical modeling, and production control systems can be just as absorbing as AI.
A PhD in Machine Learning is the Calling Card of the Engineers Advancing the State-of-the-Art in AI
As the highest level of academic achievement, a PhD program in machine learning can take anywhere from four to six years to earn. If you are coming at it with a master’s degree under your belt already, you’ll fall toward the lower end of that timeline, and may only need around 60 credits to complete the PhD. For students with only a bachelor’s, another 20 credits or so may be needed to graduate.
Some PhD programs in ML also offer the possibility of earning a master’s degree as part of the process if you don’t already have one.
By its nature, an ML PhD is steeped in research. As much as half the time spent in doctoral studies will be invested in groundbreaking investigations that fuel a required dissertation project. At this level, you work side-by-side with your professors, exploring new ideas and developing your own approach to ML challenges.
The result of such intensive studies is an unparalleled skillset in:
- Statistics
- Mathematics
- Neural networks
- Learning theory
When it comes to AI engineering, this puts you into a rarified subset of people who have the most advanced qualifications in advanced algorithmic development and neural networks. It doesn’t cover many of the other kinds of tools or broader use cases of AI. But there’s plenty of room for growth in AI machine learning development alone… and that’s before you get to all the other options you have as an ML expert.
A PhD in Machine Learning Unlocks Job Opportunities in AI and Beyond
That skillset opens up a wide range of jobs in a variety of industries. Your possibilities are even broader than with a doctoral degree in AI, since ML has so many other niche applications that are already thriving.
Of course, you will find jobs opening up in academia too, but there’s an even stronger demand in the private sector where machine learning is being advanced and applied in novel ways. These computer science and research-heavy roles are where new algorithms are developed and new tools are generated, from generative language systems to breakthroughs in neural network training.
ML PhDs also open up plenty of positions in fields like data science and information technology. Everything from automated recommendation systems to email spam filtering is rooted in advanced ML algorithms.
Naturally, achieving the top educational credential in such a hot field is likely to unlock top salaries, as well. You can find out more in our Machine Learning Salary Guide.
Machine Learning PhD Programs Often Specialize in Different Roles
Since machine learning has such a wide range of applications, you’ll find that there are a wide range of PhD programs that specialize in all those aspects of the field.
While some truly do focus on the agnostic center of math and algorithms at the core of ML science, you’ll typically find that an ML PhD will lean toward one of three general categories:
Artificial Intelligence
You will find plenty of PhD programs out there today that are squarely focused on AI applications of machine learning. Frequently, they will have titles like a PhD in Machine Learning and Artificial Intelligence, or Doctor of Engineering (DEng) in Artificial Intelligence and Machine Learning. But they may simply be offered as PhD in Machine Learning degrees, but with a very AI-focused set of coursework and research requirements.
Data Science
Data science was hot long before the AI revolution really took off in recent years. So many of the oldest doctoral programs in ML now are very DS-focused. Like the AI-centered programs, they may not have data science in the title, but you can tell from the research areas and coursework offered. In other cases, as with a PhD in Statistics Machine Learning and Big Data, or PhD in Computational Science and Engineering Machine Learning and Data Science Option, it’s quite clear where they are taking you.
Other Specific Professional Applications in Machine Learning
These are programs that dive into very specific fields that are effectively sub-fields of ML, in the same way that ML is a sub-field of AI. They can include computer vision PhD programs like a PhD in Computer Science Concentrating in Computer Vision, or a PhD in Computer Science focused on Natural Language Processing. While they may not specify ML in the title, they effectively work in fields where ML is the name of the game today.
You can also consider various other majors with strong ML emphasis options to fall into this category. That can include a PhD in Statistics with Designated Emphasis in Computational and Data Science and Engineering, a PhD in Industrial Engineering and Operations Research with Machine Learning and Data Science concentration, or a PhD in Computer Science Machine Learning Specialization.
One PhD is tough enough, but in some cases, putting together dual doctoral programs is the best way to specialize in new areas of ML applications.
Also along these lines, many schools offer dual or joint PhD programs that combine ML with other professional studies. A joint JD/PhD in Machine Learning can turn you into a lawyer with in-depth expertise in a field that is sure to dominate courtrooms throughout the 21st Century. Similarly, a joint PhD in Machine Learning and Public Policy will equip you to help answer hard questions about how AI and automated systems should be regulated. An MD/PhD in Machine Learning puts together cutting-edge ideas in ML with medical expertise that can fuel breakthroughs that might save untold millions of lives.
Many schools are quite flexible when it comes to combining advanced studies, so many will allow you to put together doctoral studies in various combinations—if you can hack it. You’ll find options like:
- Joint PhD in Machine Learning and Statistics
- Joint PhD in Neural Computation and Machine Learning
- Dual PhD in Machine Learning and Master of Business Administration
The Flexibility in Machine Learning PhD Program Curriculum Is Your Key to Specialization
It’s entirely possible to align more general ML PhD programs with whatever specialization holds your interest. In effect, just about every ML PhD is a customized course of study. The coursework and research projects are a unique set of challenges set up by you and your professors to specifically investigate your area of interest. The most important factor in choosing a program will be identifying one that has the resources and expertise to guide you down that path.
Different schools approach coursework requirements differently at the doctoral level. In some, you will have a core set of graduate study requirements in areas like:
- Computational Statistics
- Statistical Theory and Modeling
- Algorithmic Design and Optimization
- Databases and Big Data Processing
Required coursework in machine learning PhDs is usually minimal, but the specific courses usually reflect the focus of the degree program overall.
In others, your only requirements may be various doctoral seminar courses. These are intentionally more general in nature, covering your research projects and dissertation writing. To support that work, there may also be required courses in research design and academic writing.
Apart from that, the required credits needed for graduation will come out of your custom selection of graduate-level coursework offered at the college. Together with your advisor, you can put these together in almost any combination that fuels your ML studies toward new developments in your focus area.
The Doctoral Dissertation Is the Rock on Which Machine Learning PhD Accomplishments Will Rest
That focus area is also the main subject of the most important part of an ML PhD: your doctoral dissertation.
A dissertation may go above 200 pages of tight, well-reasoned, academic writing. You’ll have to present and defend it in front of a dissertation committee of professors and experts in the field. It will represent the sum of your research, your thinking, and your analysis of existing developments in the field.
But while you will draw on the state-of-the-art, you’re also expected to push it even further. Major breakthroughs in the field have come out of dissertation research: Chelsea Finn’s algorithms for meta-learning have dramatically reduced dataset size requirements for deep learning, and Marc Bellemare’s work in reinforcement learning algorithms using the Atari 2600 game platform revolutionized ML training techniques.
To stand in such company, you have to be able to produce nothing less than your very best. Future employers will zero in on your dissertation work almost immediately. Your career will be launched on the basis of what you accomplish. Whatever topic you decide to focus on, it’s the foundation of your career in ML.
Best Machine Learning PhD Programs – Where to Earn Your PhD in Machine Learning
Interestingly, you have more choices among schools offering ML PhD programs than if you were looking at studying artificial intelligence more broadly at the doctoral level. And more options mean more opportunities to find the program that’s involved in exactly the kind of research that aligns with your interests.
What you’re looking for is a program with sufficient funding, research resources, and professors whose investigative interests and expertise are well aligned with your career objectives. Considering how closely PhD candidates and their advisors work together, even personality comes into consideration.
Naturally, you will also want a school that has strong ties to the industry you’re interested in working in. That helps build your network and opens opportunities for internships or fellowships at companies where the hottest developments are being unlocked.
In a Competitive Machine Learning PhD Program, the Vetting Goes in Both Directions
Of course, just as you are looking at what ML PhD programs have to offer you, they are looking back at you just as carefully. Getting into these programs isn’t easy. Schools want candidates who bring new ideas to the table, a strong work ethic, and the kind of passion and drive that ensures a real chance at succeeding in the program and contributing something meaningful to the field.
The slots for new candidates are limited in most doctoral programs. Even the largest admit only a handful of PhD students each year. It’s the price you pay for such individualized attention and assistance. And your own personality and ideas will matter—potential advisors will assess you carefully to make sure you are someone department faculty can cooperate with closely and count on for five or more years.
To build a strong case for yourself, you’ll need to submit:
- College transcripts showing a high GPA and the right kind of foundational coursework in math and computer science
- •Multiple strong letters of recommendation from previous professors and supervisors
- A well-written statement of purpose outlining your goals and research intentions
- A CV showing relevant work or research experience in the field
If you make the initial cut, you can expect to face a serious interview process with your potential advisors and professors. Only if you click and clear the bars for entry can you expect to be accepted.
How Much Does It Cost to Earn a PhD in Machine Learning?
Of course, once you get in, you still have to find a way to pay for your PhD in ML.
The costs are steep. According to 2022 data from the National Center for Education Statistics, the average cost in tuition and fees for graduate studies in American universities came to $20,513.
Over a five-year program, the average cost in tuition and fees amounts to more than $102,000.
Elite private schools will cost even more: $28,017 per year according to NCES. But it’s also possible to go the other direction. Some excellent public universities offer more manageable tuition rates for top-notch education for in-state students, averaging only $12,596 per year.
Still, that’s a heavy lift for many PhD candidates. Student loans are common. On the other hand, the substantial paychecks that come with top qualifications in ML can make those payments less formidable.
Some schools also deliver full or partial funding for top candidates through fellowships or graduate assistantship positions. These opportunities may also factor into your choice of university.
Online PhD in Machine Learning Programs are Rare
A doctoral program is traditionally so intimate and personal that relatively few online options are offered, unlike what you’d find in master’s and undergraduate degrees. So, you won’t find the same wide selection of online PhD programs in machine learning.
On the other hand, it’s a field that is such a natural fit for remote studies that a handful of online or hybrid options exist. On top of that, the individualized and extended nature of ML PhDs means that many candidates can work out flexible attendance plans with their advisors.
The advantages to remote studies are clear. At the time in your life you are likely to be pursuing a PhD, you are more likely to have an existing career, a family, and other kinds of obligations that get in the way of more traditional studies. With asynchronous courses and communication, you can shift your education around your other commitments.
Avoiding having to relocate for four or more years is another big deal for similar reasons. You can also keep the costs down by sticking close to home.
If you have a strong investment in being the best, a PhD is an obvious goal. And if you like the idea of making waves in the hot new field of AI, but also want to keep your options open, then a PhD in ML is a good choice.