Artificial Intelligence Master’s: What Are the Admissions Requirements?

There’s no denying the stats—artificial intelligence (AI) jobs are on the rise and top talent easily commands a six-figure salary. LinkedIn’s Emerging Job Report noted that artificial intelligence, robotics, and data science roles demonstrate the best potential for growth in the year ahead. Hiring for artificial intelligence specialists alone has seen an impressive 74% increase in the past four years.

If you’ve been considering going back to school for artificial intelligence or machine learning, you already know the benefits of having that additional degree on your resume. For one, graduate students earn a higher average salary than those with just a bachelor’s degree. Graduate programs also connect you with top experts in the field, cutting-edge research projects, and interdisciplinary coursework crucial for landing unique positions.

So here’s the big question: what are the prerequisites for an artificial intelligence program? Top master’s programs are competitive—the acceptance rate for Carnegie Mellon’s program is just 10 percent. But with a strong resume, real-world experience, a foundation of computer science courses, and a great GPA, you can earn a coveted spot in your ideal degree program.

In this article, we’ll go over:

• What is an artificial intelligence master’s degree?
• What are the admission requirements at top programs?
• Curriculum breakdown for AI programs
• Specialization and concentration options
• What are the common career paths in artificial intelligence?
• Which industries are hiring artificial intelligence professionals?
• What are the top artificial intelligence jobs in each field (and how much do they pay)?

What is an artificial intelligence master’s degree?

You will typically find master’s programs in areas like artificial intelligence and machine learning based within a school’s computer science or engineering department. Some top schools also offer graduate certificates for career development or as a way to supplement a larger master’s degree.

Most master’s curriculum in AI and ML allows students to specialize further. Elective courses outine a student’s intended career path and industry, as well as unique areas like natural language processing, deep learning, or big data.

A master’s traditionally takes between two and five years to complete, depending on whether the school offers a full-time or part-time schedule.

Above all, a master of science in artificial intelligence aims to prepare students to push the boundaries of research and product development in today’s rapidly evolving field. Core courses provide a foundation of the industry, while business electives help graduates translate their findings into useful and profitable products.

What are the admission requirements at top programs?

Highly-regarded programs may have lower acceptance rates, but they typically require the same admissions materials as less competitive schools.

Carnegie Mellon’s MSAII program , for example, requires students to have a 3.0 undergraduate GPA, competitive GRE scores, and a minimum Test of English as a Foreign Language (TOEFL) score of 100 for international students. Students also must submit transcripts, a resume, a statement of purpose, three letters of recommendation, and a short video introducing themselves.

This list is pretty standard for admissions, but other programs may have additional requirements. Take a look at Southern Methodist University’s online program. Students do not necessarily need to have a bachelor’s in computer science for admission, but they may have to take prerequisite courses to ensure they have the foundational knowledge they’ll need to successfully complete this graduate program. Applicants also can waive the GRE if they have two years of professional experience.

It’s important to note that some schools have waived GRE or GMAT scores due to the COVID-19 pandemic.

Curriculum breakdown for AI programs

Prerequisite courses and minimum test scores all come down to ensuring each cohort is on the same page. Students may come from different backgrounds in the field, but should have similar foundational knowledge that will allow them to take on the more complex topics of AI and machine learning.

Universities typically break their curriculum into foundation courses, advanced courses, and electives. In some cases, the foundation courses differ depending on your chosen concentration.

Foundation courses

Students explore the overarching theories, technology, and research for AI systems. They also may dive more deeply into related mathematics, such as statistics and advanced algorithms.

Advanced courses

With this coursework, you’ll explore common AI topics like neural networks, deep learning, natural language processing, and computer vision. Programs will cover today’s top AI software, languages, and products, as well as in-depth research skills within the school’s lab.

Electives

Not all schools will provide students with the opportunity to specialize further than AI or machine learning, but most require electives to hone in on a student’s unique interests. They can choose from specific topics like cyber security or focus on skill areas like software development.

At the end of the program, students typically also have the option to complete a capstone project or write a thesis.

Specialization and concentration options

Universities understand earning a master’s is not always necessary to land a job in AI, so customizing your education is key. If you’re dreaming of working in healthcare, you may focus on classes related to systems biology more than a student hoping to build self-driving cars.

Both full-time and part-time remote programs encourage students to choose a concentration. Drexel University, for instance, even allows students to “stack” up to three graduate certificates and apply them toward a master’s.

Stevens Institute of Technology is a great example of a school with a long list of concentration options. They include artificial intelligence in design and construction, data engineering, and electrical engineering, just to name a few.

What are the common career paths in artificial intelligence?

Artificial intelligence’s expansive reach means that you’ll find a wide range of job postings related to this field. While you’ll spot plenty of software engineer roles, opportunities exist in nearly every department in almost every field, from marketing to product development.

The most common AI career paths in 2021/2022, according to San Diego University, include:

• Researchers and research scientists: Drive the research efforts behind developing new technology, applying math and statistics to a company’s findings, and advancing the use of AI in the company.
• Machine learning engineer: Focus on technology that expands a machine’s ability to mimic memory and learning. Engineers translate research into the construction and prototyping of a product.
• Data scientist: Translate datasets from AI and ML research to improve software or best use the product in a company. Data experts also may translate big data to help companies and industries communicate findings more effectively.
• Business intelligence: Use market data to make decisions about investing or developing new technology to keep up with opportunities in the field.

Artificial intelligence professionals also work to support research and development positions in other departments. These may include business management, marketing, finance, or operations roles.

You also should keep in mind that common job titles in AI, such as software engineer and researcher will mean different things at each company. Since the technology is constantly evolving, the responsibilities of each position will change as well. So, it’s important to dive deeply into each job posting to make sure it’s the right fit with your background.

Which industries are hiring artificial intelligence professionals?

When you write your statement of purpose for your application, some schools will request that you outline your career goals post-graduation. Artificial intelligence may be on the verge of being incorporated in nearly every industry, but what are the top ones hiring right now?

Let’s take another look at LinkedIn’s Emerging Report. The top industries hiring within the field of AI include:

• Higher education: Organizing large data and predicting the future of student trends aims to streamline recruitment, the application process, and even building curriculum.
• Information tech and services: Smart systems can revolutionize the security, capacity, and accessibility of today’s most popular tech services. AI can also help programmers overcome bugs, improve server connections, and assist customers.
• Consumer electronics: As one of the largest industries for AI and ML technology, this covers everything from your robot vacuum to your smart home device.

Other industries emerging as top contenders in the AI job field include healthcare, marketing, and employee recruitment.

What are the top artificial intelligence jobs in each field (and how much do they pay)?

As we noted above, AI job titles have the habit of encompassing a range of responsibilities, but you will see plenty of patterns. For example, CNBC noted these positions as the most in-demand in 2021:

• Data Scientist: AI products utilize massive amounts of data to build algorithms and detect trends, but you need a specialist to gather, hone, and transform the data into new technology. These roles earn an average of $110,000 a year.
• Senior Software Engineer: These leadership roles require extensive training in programming languages, data structures, and other advanced AI concepts. Roles typically pay an average of $120,000 a year.
• Machine Learning Engineer: While similar to AI, machine learning engineers focus on a computer’s ability to learn and make decisions. Candidates may need specific experience in areas like natural language processing or computer vision. These roles bring in an average of $125,000.

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