Have you ever wondered how companies like Spotify and Netflix generate such accurate personalized recommendations? The secret is machine learning, the application of data analysis algorithms to your browsing history to infer your tastes.
Machine learning works surprisingly well. How well? Roughly 80 percent of the time people spend on Netflix involves watching movies and shows chosen for them by the site’s recommendation algorithm.
Many people aren’t aware that the recommendations they see on Netflix are unique, but they are. As Todd Yellin, Vice President of Product at Netflix, explains: “Our personalization enables us to create more than 250 million tailored experiences to delight each user every single time they enter the platform.”
Machine learning was once considered an esoteric discipline, a curiosity talked about in computer science circles. Now, not only does machine learning inspire impulse buys on Amazon, but it may be, as Steven Levy believes, “the true path towards imbuing computers with the powers of humans, and in some cases, superhumans.”
Of course, Levy’s vision of computers with superhuman abilities hasn’t been realized just yet, and probably won’t be for some time. Right now, machine learning engineering is driving the evolution of predictive text and helping Google understand what you’re looking for before you do. We’re moving into a future, however, in which cars will be able to drive better than humans, computers are our primary care doctors, and machines take over jobs we once thought only humans were capable of, like artist, social worker, or philosopher.
One way to prepare to become a part of that future is to earn a master’s degree in machine learning.
In this article, we answer the question what is a master’s in machine learning? and cover:
Artificial intelligence, or AI, refers to thinking machines. It’s an umbrella term that encompasses everything that goes into the technology that lets machines think, from the hardware to the software systems involved. Machine learning__ is part of artificial intelligence—specifically the computational algorithms that let computers gather data, examine it, understand it, and use it to make smarter decisions when doing things like recommending movie titles or determining how to navigate a sticky traffic situation.
What makes machine learning different from traditional programming is that machine learning code doesn’t tell computers what to do. Rather, it provides them with large data sets and a framework in which they can identify patterns and use them to make predictions about the future. With machine learning algorithms, computers can be trained to map out floor plans, create luxury car commercials, brew custom beer, and behave like bees.
The tech industry is known for welcoming those with significant hands-on experience. Plus, the demand for AI professionals is rapidly increasing. Many AI job postings you’ll spot today will only require an undergrad degree and a list of specific required skills. However, when you browse highly sought-after positions at well-known firms, the jobs often require a graduate degree (PhD or computer science master’s). If you’re looking for high-paying positions in the field, a master’s is crucial. (
The earning potential for these roles typically soars past the six-figure mark. The median salary of an AI engineer, for example, is $164,800. A University of San Diego salary chart lists other six-figure AI-related positions, including data scientists (who earn $127,000), research scientists ($111,000), and big data engineers ($131,000). ( )
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Machine learning is still a puzzle waiting to be solved. As Dave Waters once observed, “A baby learns to crawl, walk and then run. We are in the crawling stage when it comes to applying machine learning.”
The people most likely to go all-in on machine learning are technically adept, with strong analytical, math, programming, and problem-solving skills. They’re also creative and curious. Intensely curious! They’re passionate about AI, and they’re driven to know how far they can take it. They may end up working in finance or cyber security—in fact, they probably will. In PwC’s 2020 AI Report, the company predicts that “much of the AI excitement will come from results that may sound mundane: incremental productivity gains for in-house [business] processes” like identifying fraud and automating reporting.
Even so, what inspires people to pursue degrees in machine learning isn’t the desire to find new ways to extract information from tax forms. Rather, it’s an intense need to determine what computers are capable of given the right programming.
Naming conventions differ from school to school, but you’ll quickly discover in your school search that there are very few dedicated master’s in machine learning programs. A few colleges and universities offer full-time and part-time Master of Science in Machine Learning programs, but you might also pursue any of the following degrees:
Machine learning is an increasingly important part of AI, computer science, computer engineering, and data science, and many colleges and universities group these disciplines. At Duke University, for instance, data analytics and machine learning master’s programs are combined. Students can choose from various related academic pathways, including:
As you might expect, machine learning isn’t a topic you can jump into at the master’s level. Most colleges and universities require applicants to hold a bachelor’s degree in computer science, software engineering, data analytics, or a related STEM field. In some cases, you’ll also need to have some professional experience in computer science, software engineering, or data science.
Nearly all programs require applicants to submit GRE scores, though some offer waivers to students with exceptional academic records or work experience. Be aware that admissions to the best master’s in machine learning programs are highly competitive. Stanford University, for example, receives about 3,000 applications annually for its computer science program; it accepts just a handful of students.
Coursework in machine learning master’s programs can cover a broad range of topics related to applied AI, theoretical AI, and data science. You might take core courses or electives like:
The core curriculum in most master’s in machine learning programs won’t include programming, math, or statistics classes. Programs expect students to arrive with a solid grasp of these subjects. At Carnegie Mellon University, for instance, students in the Master of Science in Machine Learning program are expected to have “some reasonable programming skills, with experience in Matlab/R/scipy-numpy especially helpful, and Java and Python being more useful than C, and a solid math background, especially in probability/statistics, linear algebra, and matrix and tensor calculus.”
Master’s in machine learning programs require students to do hands-on work, which can take the form of an internship or independent research project that can lead to a publishing credit. However, in many programs, a capstone course stands in for an internship. During the course, students work in small groups or larger teams to solve a machine learning problem.
As is the case with most master’s degree programs, earning an MS in Machine Learning or a related degree can take anywhere from one to three years or more. Two-year programs are the most common, but it’s possible to graduate faster in accelerated full-time programs or to study five years or even longer before graduating in some more relaxed part-time programs.
The Master of Science in Computer Science with a focus on machine learning program at Stanford University, for instance, takes three years minimum to complete. In contrast, students in the Master of Science in Machine Learning program at Carnegie Mellon University can complete the program in just one year.
Not all the below colleges and universities offer a master’s in machine learning, but every school on the list offers a degree related to artificial intelligence or data science that can be customized through concentrations or electives to prepare you for a career in this discipline.
The quick answer is well-paying ones in just about every industry. Jobs in artificial intelligence used to be something about which sci-fi authors wrote. Now everyone, from financiers to supply chain managers, is looking at the potential of machine learning to automate and streamline processes, reduce inefficiencies, and make it easier to make money.
According to the Bureau of Labor Statistics, machine learning jobs and other jobs in computer research pay about $123,000. The highest 10 percent of people in this area of computer science earn more than $190,000 per year. Even entry-level positions in machine learning can pay above $75,000, and the average machine learning engineer earns $114,000.
Other titles people have after graduating with a master’s in machine learning include:
A career in machine learning should also be a stable one. While it’s likely that the evolution of machine learning will almost certainly automate some jobs out of existence, the research firm Gartner predicts that machine learning will create more jobs than it eliminates—to the tune of 2.3 million new jobs by 2025. LinkedIn’s 2020 US Emerging Jobs Report put machine learning jobs at the top of its list, noting that hiring for this role has grown 74 percent annually in the past four years.
The best reason, however, to look into master’s in machine learning programs isn’t that you’ll earn excellent money or have job stability. Those things matter, but not nearly as much as loving what you do. Some people are just born to push the envelope where computers are concerned. For them, a career in machine learning will be full of excitement, fun, and infinite possibilities. As one Quora commenter put it in a thread about what’s so exciting about machine learning: “On a more philosophical level, humans have always been obsessed with recreating the most powerful computer we know—the human brain. Machine learning is taking us closer to that goal.”
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