Artificial intelligence and machine learning play a crucial role in our daily lives. With every package that’s delivered, every notification we get on our phones, and every post we see on social media, we’re interacting with AI & ML algorithms and models — and our encounters with artificial intelligence and machine learning will only increase in frequency in decades to come. Grand View Research projects the global AI market to expand by over 40% by 2028, topping $900 trillion worldwide. That’s more than 200x the annual GDP of Germany!
With artificial intelligence’s current ubiquity and its staggering growth potential, it’s no surprise that more and more students are seeking advanced training in AI. 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.
Graduates of computer science, artificial intelligence, and machine learning master’s programs are entering a booming job market. 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.
This strong demand is translating into lucrative salaries for AI professionals: 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 salary of $56,287.
For those college graduates interested in helping build the next generation of artificial intelligence technologies, the path seems simple: apply to an artificial intelligence or machine learning master’s program, head to campus for two years, and have your pick of amazing job opportunities when you get out, regardless of whether you want to be an artificial intelligence engineer or machine learning engineer, or even work in applied data science or business analytics.
But for many — and maybe also for you — it’s not that easy. Higher education is expensive, so for many, graduate study seems out of reach, especially if there are still undergraduate loans to pay off. The price tag grows when you factor in costs like relocation expenses and lost earning potential if you need to stop working while you study.
There are other hurdles to overcome as well. Even if you’re studying nearby, being on campus for four semesters means being away from your loved ones — something that is especially tricky if you’re also a parent or other caregiver. And things only get more difficult when you are an international student faced with relocating to another country entirely.
If COVID-19 has taught us anything, however, it’s that online study is a viable option, one that offers flexibility and affordability that for many is enough of a push to log on and start down a new career path. Will it work for you? In this article, we’ll give you the background on master’s degrees in artificial intelligence and explore how the curriculum has been moving online in recent years. At the end, we’ll help you decide whether an online master’s in artificial intelligence is right for you and give you some criteria to keep in mind as you shop programs.
But we need to answer the most prevalent question first: what exactly is a master’s in artificial intelligence?
What’s a master’s in artificial intelligence?
Usually offered by a computer science department, a master’s in artificial intelligence is a graduate degree intended to equip students with expertise and skills in artificial intelligence, including areas like machine learning, natural language processing, and robotics. A master’s degree can serve as preparation for doctoral research or allow a graduate to deploy artificial intelligence solutions in industry.
Within this broader definition, however, there is significant room for variation in the scope and structure of a program’s curriculum. Northeastern University’s Khoury College of Computer Sciences, for example, first guides graduate students in the MS in artificial intelligence program to develop baseline expertise in artificial intelligence. After this, students can choose to specialize in robotics, machine learning, computer vision, intelligent interaction, or knowledge management.
Boston University complements their master’s curriculum with the opportunity for master’s students to pursue independent projects like an MS thesis, while Northwestern University’s McCormick School of Engineering goes further in allowing students to take one of two tracks: one for those who wish to stay in artificial intelligence and one for those with advanced degrees who wish to combine artificial intelligence and their home discipline in interdisciplinary study.
Who’s a master’s in artificial intelligence for?
Applicants to master’s programs in AI generally apply while or after completing a bachelor’s degree in computer science, applied mathematics, engineering, or some other technical field. That said, many artificial intelligence master’s programs will accept students with bachelor's degree in other areas provided they can demonstrate sufficient knowledge in computer science and mathematics or complete remedial courses either directly prior to or in the first semester of study.
What does a master’s in artificial intelligence teach?
Even if you enter a master’s program in artificial intelligence with prior experience in computer science, you’ll likely begin your course of study with courses aimed at equipping students with a baseline knowledge in the artificial intelligence, such as Northwestern’s “Introduction to AI,” “Machine Learning,” “Frameworks for Artificial Intelligence,” and “Data Science Seminar.” BU offers similar courses with titles like “Image and Video Computing,” “Artificial Intelligence,” “Introduction to Natural Language,” and “Machine Learning.”
Survey courses like these give graduate students a foundation in the basics of AI and initial opportunities to apply what they’ve learned. At the University of Georgia’s Institute for Artificial Intelligence, for example, students in the machine learning survey course learn AI techniques “selected from inductive learning, decision trees, neural network approaches, reinforcement learning” and then apply this knowledge in cases “selected from data mining, medical diagnosis, fraud detection, pattern recognition, and/or other contemporary applications.”
Typically, completing these survey courses will allow students to pursue advanced topics such as deep learning, data mining, and applied data science. Oftentimes they will also complete a practicum, portfolio, internship, capstone project, or thesis to close out their course of study. These opportunities offer crucial career preparation by allowing graduate students to apply what they’ve learned.
How is an online master’s in artificial intelligence different from an in-person degree?
We’ve covered the basic curriculum of an artificial intelligence master’s program and the target audience of these programs. Now, let’s move on to exploring what changes and what stays the same as these programs move online.
Curriculum
For the most part, the curricula for in-person and online programs will be identical. Artificial intelligence and machine learning are digitally native fields, so moving instruction online doesn’t require too much of a shift. As an example, take Yeshiva University’s M.S. in Artificial Intelligence online program, which features courses you wouldn’t be surprised to see in an online program like “Neural Networks and Deep Learning,” “Natural Language Processing,” “Data Visualization,” “Advanced Data Engineering,” and “Predictive Models.” As with in-person programs, online there are also opportunities to pursue independent study or an internship.
Enrolling in an online AI master’s program also might not mean that all your learning will be online. Johns Hopkins University’s Artificial Intelligence Master’s, for example, offers core courses that can be taken online at home but gives students the choice of taking electives like “Neural Networks” and “Deep Learning for Computer Vision” face-to-face on campus or in a “Virtual Live” modality.
Campus Experience
As would be expected, those choosing to learn online won’t typically have a robust campus experience — with the exception, of course, of programs like Johns Hopkins’. While foregoing a campus experience certainly has its upsides if you have obligations at home or want to save money, not being on campus can potentially mean you’ll miss out on some great in-person resources and events. Of course, this is not to say that there aren’t great online alternatives, especially since the onset of the pandemic.
Networking
As with resources and events, networking also requires some rethinking in an online educational environment. If you want to pursue a career in AI, networking will be crucial, and the relationships you make in graduate school will form a core of your network for years to come. While you won’t necessarily get to share a beer with your classmates, social media and Zoom parties can be great alternatives to help you get to know your colleagues.
Internships
While universities often partner with local corporations to offer internships for their on-campus students, in an online setting, this might not be the case. But because artificial intelligence is already digitally native, there are plenty of opportunities to intern remotely. Alternatively, you can find an internship with a corporation in your hometown.
Flexibility
Studying online, especially if the curriculum is asynchronous (i.e. watching video lectures and completing readings and exercises on your own time) is extremely flexible. In many cases, students can fit in their graduate studies around their job, family obligations, athletic regimen, or improv group. While this flexibility might be good for your headspace, it also has a financial upside: if you can keep working and taking care of your family while you learn, you can reduce both unrealized income and expenditures.
Cost
Higher education is pricy in the US. Growth in the size of university administrations and the luxury of college campuses has pushed tuitions higher than they’ve ever been, with a year of graduate study averaging $27,776 for non-profit private institutions and $12,171 for public institutions (in-state) in 2018.
As we’ve just shown, learning online can potentially offer cost savings by allowing you to stay put, keep working, and care for your family. According to U.S. News and World Report, online tuitions can also be lower: “the average per credit price for online programs at the 168 private colleges that reported this information is $488 – lower than the average tuition price for on-campus programs at ranked private colleges, which is $1,240 among the 113 colleges that reported this information.”
We should note, however, that not all agree regarding the potential cost-benefit offered by online degree programs compared to in-person study, so make sure to crunch the numbers yourself.
Studying artificial intelligence in person or online: which is right for you?
We’ve just covered how cost can influence your decision to study in person or online. Here are some other aspects to think about:
Admissions Requirements & Prerequisites
It’s essential to ensure that you meet any admissions requirements and prerequisites before starting your application, so if you’re interested in a school check out their website to see if they require a certain GPA or that you submit GRE scores. If you’re unsure whether you qualify, feel free to email the admissions office to see if your application will be welcome.
Curriculum Specifics
Just like teams or corporations, AI departments have different strengths. Accordingly, the curricula of master’s programs will vary with the department’s identity. As you’re researching, pay attention to how a department or program is portraying itself, and make sure that matches up with what interests you and where you might want to go, whether it’s working with big data or natural language processing.
Student Outcomes
If a program isn’t putting out students who find positions in academia or industry, it’s not worth the tuition. Make sure to check out any outcome information a program lists, and supplement this information by using LinkedIn to research where alumni have ended up.
How can we help?
It’s already a big decision to choose a program (or programs) to apply to, and that decision isn’t made any easier when you also are deciding between studying in-person or online. Ultimately, the decision will be yours: you’re the expert on your current situation and your goals for the future.
That said, we’d like to help. To make your decision easier, we’ve vetted in-person and online master’s programs in artificial intelligence and assembled our favorites in user-friendly guides. 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.
Remember: artificial intelligence is the future. The application process might seem daunting now, but future-you will be thankful for your due diligence and hard work in the present.