The need for machine learning engineers — and the astronomical salaries qualified professionals earn as a result — led LinkedIn to place machine learning engineer fourth on its 2022 Jobs on the Rise list, citing a salary range of $72,600-$170,000. Indeed puts the average machine learning engineer salary in the US within this range at $124,059, far higher than the average annual salary in the country, which the U.S. Bureau of Labor Statistics pegged at $58,260 in 2021.
Unsurprisingly given the favorable job market and high compensation, master’s degrees in machine learning are seeing accelerating demand, with universities heeding the call. Stanford’s AI Index notes that the 73 respondents to its global survey of leading universities reported increasing graduate offerings teaching practical artificial intelligence and machine learning skills by 40% over the four academic years leading up to 2020, with faculty specializing in AI & ML growing by almost 60% over the same timeframe.
Are you considering a future in machine learning, but don’t know if a master’s degree is right for you? We’re here to help. In this article, we’ll cover exactly what to expect from master’s-level study in machine learning, who can thrive in these kinds of programs, and what your career path could look like if you graduate with a master’s in machine learning. We’ll also give you some advice on how to structure your decision process and preview some great machine learning programs along the way.
What’s a master’s in machine learning?
A master’s in machine learning is an advanced degree that’s intended to familiarize students with the landscape of machine learning — including areas like deep learning, reinforcement learning, and big data analytics — as well as give students the skills and other practical knowledge they need to undertake doctoral research or build and deploy machine learning models in the industry.
But a master’s in machine learning — or a program serving these learning goals — won’t always be packaged as such. Take, for example, the following three programs:
MS in Machine Learning at Carnegie Mellon University
Carnegie Mellon’s Master’s in Machine Learning is a two-year program that requires students to take six core courses, choose three elective courses, and complete a practicum.
Probability & Mathematical Statistics or Intermediate Statistics
MS in Computer Science w/ ML Specialization at Georgia Tech
Georgia Tech offers graduate machine learning study through an ML specialization offered within their computer science master’s degree. The machine learning specialization is one of 11 specializations offered, with students expected to take five courses in the specialization (two core courses and three ML electives) and five general elective courses in other areas of computer science.
Machine Learning Core Courses
Machine Learning Electives (Selection)
At Georgia Tech, master’s students are also able to choose between a course option, where they take 30 hours of coursework, and a project option, where they take 21 hours of coursework and complete an independent machine learning project under the supervision of an advisor, or a thesis option, where they complete 18 hours of coursework and write a thesis about machine learning research they’ve completed while studying.
MS in Machine Learning and Artificial Intelligence
Drexel University offers a master’s degree that combines broader artificial intelligence instruction with more specialized machine learning training. Possible to complete either part- or full-time, online or on campus, Drexel’s program requires that students take five core courses, three elective courses distributed across the areas of Data Science and Analytics, Foundations of Computation and Algorithms, and Applications of Artificial Intelligence and Machine learning, seven additional elective courses, and a capstone course in which students complete an artificial intelligence project.
AI & ML Core Courses (ML Concentration)
AI & ML Electives (Selection)
As you can see, master’s-level machine learning training can be variously designated, but the core skills and expertise you develop are the same. Any worthwhile program will offer training in programming languages, statistics, and linear algebra (if needed), the different kinds of machine learning (e.g. unsupervised learning, supervised learning, reinforcement learning, deep learning), and how to write various types of machine learning algorithms and models. Through electives, you can also choose to further explore how machine learning factors into crucial areas such as big data analysis, computer vision, and natural language processing.
Who’s a master’s in machine learning for?
In general, applicants to machine learning master’s programs are expected to have earned their bachelor’s degrees in computer science, mathematics, or another technical field.
There are certainly instances of industrious students breaking into machine learning with a bachelor’s in the social sciences or the humanities, or even experienced professionals in other fields transitioning to machine learning later in their careers. But to gain admission to a master’s program these individuals will need to demonstrate skills in computer science and advanced mathematics or take introductory courses before they begin their master's degree — or both.
That’s who goes in, however. Who comes out of the graduate study in machine learning?
What opportunities are out there for newly minted masters in machine learning?
Many graduates of ML master’s programs will go on to be machine learning engineers, but opportunities in data science and data and software engineering are also available. Here’s how they differ:
Machine learning engineer
An ML engineer design, develop, and ships machine learning models — and then upkeep them once they’re shipped and deployed to a live product.
A data scientist ideates and executes novel approaches that turn raw data into business insights and solutions using machine learning and other data analytics.
A data engineer focuses on building, streamlining, and maintaining the data pipelines that are central to data science, machine learning, and other data analytics.
A software engineer specializing in artificial intelligence or machine learning system design focuses on building and maintaining a particular product (an app, API, or service) that brings AI capabilities to consumers and businesses alike.
You can find more information on detailed responsibilities, qualifications, and salaries at our Careers portal.
How to choose the right machine learning master’s program?
If a master’s in machine learning seems like the right path for you, you’ll need to start thinking about how you can make the most of your time in school. That starts with ensuring you find a good fit. Here are some things to keep in mind as you research programs:
Admissions Requirements & Prerequisites
Before you fall in love with a program and start assembling your application, it’s crucial to ascertain whether your application will be welcome. When schools list requirements and prerequisites for study, they mean it. As you research, make sure you take note of a school’s requirements for:
Graduate Record Examination (GRE) scores
Personal statements, transcripts, and other materials you must submit
TOEFL score or other English proficiency requirement (if applicable)
Though we’ve shown you some similarities between them, not all master’s programs are created equal: the specifics of program curricula will differ. When researching and deciding where you want to apply, look for information about:
Core course topics and credit hours
Elective course topics and credit hours
The department’s vision for the program
One of a program’s biggest advantages comes in the industry relationships they cultivate. These relationships yield not only internship opportunities, but potentially also job pipelines. When looking at programs, check to see if they’ve listed industry relationships on their website, or email the department directly to inquire. Failing this, you might take to LinkedIn to see whether alumni list internships they had during their studies. Often, this can be a good indicator that there is an existing relationship between a school and the company.
If COVID-19 has had any upside, it’s that it has offered proof of concept that online learning can be just as effective as learning in person — and at a fraction of the cost. If you’re not interested in relocating or having a robust campus experience, or if you’re looking to continue working as you study, check to see whether a program offers hybrid or fully online experiences. You can find our favorite hybrid and online Master's in Machine Learning programs in our recommendations section.
Before even applying to a program, you want to be sure it will get you where you want to go — there’s no use spending time and money on education if you won’t recoup this investment with a great career in machine learning. Oftentimes, departments will proudly list graduate placement on their websites. You might also (again!) hit up LinkedIn to see where alumni have ended up.
Last, but certainly not least, you want to factor a program’s cost into your calculations. When doing so, take into account:
Cost of tuition
Cost of relocation & living expenses
Lost income potential (if you take time off work to teach)
Average salaries for realistic job placements
Any grants, scholarships, stipends, or work-study opportunities
How We Can Help
With so many factors at play, it’s easy to feel overwhelmed when deciding on a program. Ultimately, the decision will be yours: you’re the expert on your current situation and your goals for the future. But to make your research and decision easier, we’ve assembled some valuable recommendations for great master’s programs in machine learning. We’ve vetted all this information to help you take out the guesswork, and we’ve put it in one place to save you time and make your research more efficient. Don’t hesitate. Bookmark it for easy reference as you continue your search.
Once you’ve landed on a set of schools you want to apply to, head over to our applications page for the latest tips on how to make your application stand out from the rest of the pile.