Master’s in Artificial Intelligence: The Low-Down

The AI market’s growing rapidly,  with  Grand View Research projecting it to expand by over 40% to over $900 trillion worldwide by 2028. With the growth of the market, so grows the AI workforce. According to Indeed, demand for those with AI skills and expertise — masters in artificial intelligence, if you will — more than doubled from 2015 to 2018, and there’s no sign this demand will slacken in the coming years. 

The growing AI market is offering lucrative salaries to those who can help it grow: Glassdoor pegs the median artificial intelligence engineer salary at $119,640 annually, and for a machine learning engineer, that median salary climbs to $124,040, both eclipsing the 2020 median US household income of $67,521.

The promise of high compensation has many students clamoring for more training in AI. Increasingly, universities across the globe are heeding the call: master’s degrees in artificial intelligence and machine learning are booming. Stanford’s AI Index notes that the 73 respondents to its global survey of leading universities reported increasing graduate offerings teaching practical AI skills by 41.7% over the four academic years leading up to 2020, with faculty specializing in AI growing by 59.1% over the same timeframe.

But what exactly does a master’s in artificial intelligence entail? Who is the course of study designed for? And, if someone’s interested, what do they need to consider as they research programs? In this article, we’ll give you the low-down to help you figure out if graduate study in artificial intelligence is right for you and how you can be sure you’re making the right choices if and when you start applying.

What’s a Master’s in Artificial Intelligence?

A master’s in artificial intelligence is an advanced degree, usually offered by a computer science department, that’s intended to familiarize students with the landscape of artificial intelligence — including areas like machine learning, natural language processing, and robotics — as well as give students the skills and other practical knowledge they need to undertake doctoral research or build and deploy artificial intelligence solutions in industry. 

While all reputable universities offering a master’s in artificial intelligence will conform to this definition, there are significant differences in the scope and structure of their respective curricula. 

At Northeastern’s Khoury College of Computer Sciences, for example, graduate students in the MS in artificial intelligence program first develop a comprehensive knowledge of artificial intelligence before choosing a specialization in robotics, machine learning, computer vision, intelligent interaction, or knowledge management. At Boston University, master's students have the opportunity to pursue independent projects, such as an MS thesis that they publicly defend in their final semester. 

Northwestern University’s McCormick School of Engineering takes an interesting approach by offering dual tracks, one for those who plan to continue in artificial intelligence and one for those with advanced degrees who plan to take what they learn and return to their home discipline. In the former, MSAI, students will complete an internship with one of Northwestern’s industry partners or work on a project in Northwestern’s artificial intelligence laboratory before finishing off with a capstone project in their final semester. In the latter, MSAI+X, students will start with a programming and math bootcamp while foregoing these later components.

Northwestern’s bootcamp requirement for students without a computer science or math background is instructive. 

In general, applicants to artificial intelligence master’s programs are expected to have gotten their bachelor’s degrees in computer science, mathematics, or another technical field. 

Though they don’t have a parallel track for students without a CS background, Northeastern similarly requires that incoming students have a strong background in computer science and mathematics. This background can be demonstrated by passing two placement exams on the fundamentals of computer science and statistics, probability, and linear algebra, respectively, or acquired before beginning study through the completion of two introductory courses.

Philadelphia’s Drexel University follows suit, requiring that students enter their Master’s in AI and Machine Learning program with a four-year bachelor’s degree or master’s degree in computer science, software engineering, or a related stem field with relevant work experience.

Even though students generally enter with CS backgrounds, you can expect future masters in artificial intelligence to begin by taking several courses to establish baseline knowledge in the field. Northwestern, for example, requires all master’s students to take “Frameworks for Artificial Intelligence,” “Introduction to AI,” “Machine Learning,” and “Data Science Seminar” in their first semester. At BU, the core requirements are instead “Introduction to Natural Language Processing,” “Machine Learning,” “Image and Video Computing,” and “Artificial Intelligence.”

These broader survey courses will provide a foundation in the basic concepts of artificial intelligence and its various subdisciplines, as well as initial opportunities to apply AI techniques and build artificially intelligent systems. The machine learning survey course at the University of Georgia’s Institute for Artificial Intelligence, for example, begins with instruction in AI techniques “selected from inductive learning, decision trees, neural network approaches, reinforcement learning,” and others, before offering opportunities to apply this knowledge in situations “selected from data mining, medical diagnosis, fraud detection, pattern recognition, and/or other contemporary applications.”

In most programs, complementing these survey courses are more advanced core classes and electives that allow students to dive deeper into more complex concepts. Northwestern, for example, requires students to take “Deep Learning from Scratch” in their first winter quarter, a course that provides an overview of neural networks and their applications. In Drexel University’s program, students are required to choose an elective in the field of data science and data analytics, with options including “Responsible Data Analysis,” “Introduction to Data Analytics,” and “Data Mining.”

As we mentioned above, in the later stages of coursework, a master’s in artificial intelligence curriculum usually culminates in a practicum, internship, portfolio, thesis, capstone project, or a combination of these that’s meant to prepare graduate students for the job market and their future careers by allowing them to apply what they’ve learned in real-life settings. And for good reason: practica and internships can sometimes turn into offers for permanent employment, while a portfolio, thesis, or capstone project can allow you to impress a hiring team and show how you work.

So you think you’re a good fit — you have the relevant credentials — and the curriculum of a master’s in artificial intelligence sounds interesting: what now? How can you be sure you’re applying to the best program for you, and how can you maximize the chances that you’ll be admitted? We’ve assembled a couple of things to keep in mind throughout the application process.

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. Their top priority is turning out stellar artificial intelligence professionals who will make an impact, whether in research or industry. Accordingly, they will only accept students who show an aptitude to thrive in their program.

Often, “aptitude to thrive” means not only having the right degrees but getting these prerequisites from the right institution and building a strong academic record while doing so. Drexel, for example, requires that applicants hold a degree from a regionally accredited institution and have earned a GPA of 3.0 or higher while there.

Sometimes, schools will also recommend or require that applicants sit the Graduate Record Examination (GRE), which assesses students’ abilities in verbal and quantitative reasoning, as well as writing. Drexel, for example, will look at a student’s application who earned lower than a 3.0 GPA during their undergraduate studies if they also include GRE scores. Boston University instead requires that students take the GRE, with the computer science department noting as a rule of thumb that admitted students on average score in the 60th percentile for verbal reasoning and the 80th percentile for quantitative reasoning.

Of course, there might be exceptions if you are a fringe case — especially if you have a compelling reason or an interesting story. When in doubt about whether you fulfill the requirements, there’s no harm in sending the admissions director, the department chair, or the director of graduate studies an email inquiry before you invest time in an application.

Curriculum Specifics

Not all master’s programs are created equal: curricula will differ based on the strengths of the faculty, the department’s vision for the program, and even a school’s location and the nearby industry connections it can cultivate.

When researching programs, you don’t need to already have an idea of where exactly in artificial intelligence you want to end up. It’s useful, however, to be self-aware if you have an idea of where you want to end up. If you’re just looking to build a baseline in AI and go from there, make sure that the programs you apply to are heavy on broader core survey courses. If you already have an inkling of what you want to focus on in your career based on your experience in undergrad or in the industry, make sure you apply to programs that can get you there through electives, internship connections, and research opportunities.

A university department is also looking for a good fit, listing their curriculum on their website to make interested students’ jobs easier. In addition to checking out these curricula, look at the make-up of the faculty to determine the strengths of the department and even identify potential formal or informal advisors. You might also seek to connect with program alumni on LinkedIn to hear their side of things and learn where they ended up. 

Industry Relationships

While we’ve already touched on this, it bears repeating: 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 again 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.

Learning Modality

If COVID 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 Artificial Intelligence programs in our recommendations section.

Student Outcomes

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 AI. Oftentimes, departments will proudly list graduate placement on their websites. You might also (again!) hit up LinkedIn to see where alumni have ended up.

If you’re looking for more quantitative data on student outcomes, you can check out our [recommendations page? Pages for each program? Outcomes page?], where we have assembled all of the verifiable outcome data out there.

Cost

Last, but certainly not least, you want to factor a program’s cost into your calculations. When doing so, take into account not just the cost of tuition, but of relocation, living expenses, and, if you will stop working to study, lost income potential, and balance these against what you’ve learned about student outcomes and average salaries for the specific roles a school’s program might prepare you for. You might also consider a state school or online program, for tuition will generally be markedly lower.

It’s also crucial to look into any scholarships or grants a department or university might offer you. While places in such programs are competitive, you might also try to gain admission to one of a handful of partially- or fully-funded master’s programs in computer science that allow you to pursue artificial intelligence while receiving a tuition remission and/or earning a modest stipend for serving as a research or teaching assistant.

With so many factors at play, it’s easy to get 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 recommendations for great master’s programs in artificial intelligence. 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. So don’t hesitate — and bookmark it so it can be your home base as you continue your search.

Once you’ve landed on a set of schools you want to apply to, head over to our Education portal for the latest tips on how to make your application stand out from the rest of the pile.