Master's in Machine Learning Programs

What is a Master’s in Machine Learning Degree?

A Master’s in Machine Learning (ML) degree is a cross-section of mathematics, statistics, and computer science. Machine Learning classes and degrees are usually offered by universities’ Colleges of Computer Science. However, some mathematics departments house the program. A complete Master’s in ML degree typically requires 30 to 45 credits. Some programs offer smaller programs that only require nine credits.

This degree prepares you to apply algorithms, mathematics, and applications to simulate human learning. ML applications use a dataset to make predictions and learn behavior, iteratively improving the predictions and behavior. A Master’s in ML degree teaches you how to identify datasets and apply algorithms to the datasets to develop business applications. These machine learning applications are utilized in a wide range of business areas, including:

• Financial services
• Educational services
• Social media and online applications
• E-commerce
• Transportation and logistics
• Manufacturing
• Health services

Industry-leading companies value Master’s in Machine Learning degrees because they demonstrate formal training in complex subjects. These complex subjects include algorithms, robotics, computer vision, linguistics, and natural language processing. Moreover, the courses often involve software development that results in a solid portfolio demonstrating expertise.

Curriculum: What You’ll Learn in a Master’s in Machine Learning Program

The majority of ML programs are specialties incorporated into Master’s of Computer Science or Masters of Electrical and Computer Engineering degrees. Most Master’s of ML programs also require one to five core classes from a list of options. For example, the Georgia Institute of Technology requires general machine learning classes in addition to algorithms. Other required courses in machine learning programs include natural language processing, deep learning, and reinforcement learning, three machine learning methods. Common electives include computer vision, networks, security, data mining, and robotics. A few programs teach ML-oriented ethics.

The core course found in most Master’s in ML programs is a graduate-level algorithms class. This class teaches fundamental skills in the design and analysis of algorithms for software applications. Graduate-level algorithms courses teach NP-completeness, i.e., how to determine the computational complexity of a problem, in addition to the most common solutions to problems found in machine learning. Machine learning engineers must be able to apply mathematical theory and statistics to data sets to develop learning applications. Some programs teach advanced statistics, such as SMU and Rice. Whereas other programs require you to have a strong background in statistics before admission, such as CSU. Many programs incorporate statistical and mathematical analysis into theoretical machine learning courses.

Many ML curriculums allow for a thesis or capstone project, such as the University of Wisconsin-Madison and Columbus State. The research projects culminate into papers and oral presentations or defenses, similar to many PhD degrees. The thesis or capstone projects are generally worth six to nine credits.

Another variance in ML programs is machine learning specialties for degrees outside of Computer Science. For example, Villanova offers a less intensive artificial intelligence and machine learning specialization for its Master’s of Business Administration degree that consists of three courses focused on how machine learning impacts businesses.

Northeastern University is one of the few programs to offer a Master’s of Applied Machine Intelligence as opposed to an ML specialization for a Master’s of Computer Science. This degree provides more depth into the study of the three most important aspects of machine learning: technology, data analysis, and human behavior. The University of Maryland is another university that offers a Master’s in ML.

Types of Master’s in Machine Learning Programs

Master’s in Machine Learning programs are usually incorporated into other master’s programs. The most common ML specialty falls under a Master’s in Computer Science. This is available at schools with robust Colleges of Computing. Therefore, in these programs, your degree will probably say “Master’s of Computer Science,” but your transcripts may indicate specialization in machine learning. On your resume, the degree would be Master’s of Computer Science, and the machine learning specialty would be described as a bullet point or subpoint under the degree.

Other universities place computing degrees in the same college as Electric Engineering. This is the second most common way machine learning master’s are earned. For example, Georgia Southern offers its machine learning education as a specialty for the Master’s of Computer Science degree, which falls under the Electrical and Computer Engineering school. There is little difference in this degree from a computer science college degree. Most experts in the industry recognize that some universities have computing degrees incorporated into the electrical engineering school instead of into a separate computer science-focused college.

The least common degrees are titled Master’s in Machine Learning. These are usually identified as “professional studies” degrees. For example, Northwestern and Rice offer their ML degrees under a professional studies college. Some schools combine artificial intelligence and machine learning specialties but then require separate courses for each. These degrees are less generalized and sometimes viewed as less rigorous than general Master’s of Computer Science degrees with a focus or specialization into machine learning. However, others view these specialized degrees as more focused on machine learning training because they do not require or allow for electives outside of machine learning.

What are the Differences Between Machine Learning and Artificial Intelligence Degree Programs?

Artificial Intelligence is a broad field that covers any software applications that replicate human behavior. Machine learning, however, requires using a dataset to train an application on future behavior. For example, the Georgia Tech Machine Learning for Trading course uses historical stock market trading data to train a machine learning model on how to optimally invest in stocks for the future. Artificial Intelligence generally covers this concept since investing is human behavior. But it does not require that the model use historical stock market data to learn how to invest in the future.

Foundational coursework for both artificial intelligence and machine learning-focused degrees covers a lot of the same content. However, machine learning requires advanced knowledge of mathematical concepts, such as statistics and discrete math. The dataset in machine learning is analyzed using these mathematical concepts. Artificial intelligence, however, focuses on broader, more dynamic techniques for replicating human behavior.

Artificial intelligence specializations are more geared towards robotics. This includes everything from manufacturing to online chatbots. The overall idea is to behave reasonably in the human world. Machine learning, however, is more targeted towards analyzing historical data, predicting the future, and behaving in an optimal way based on those predictions. Machine learning also trains robots, but it does so on data analysis techniques. So, for example, where artificial intelligence would teach robots to move materials around in a manufacturing environment, machine learning would predict the materials needed to produce the products in a manufacturing environment to optimize production.

Admissions Requirements for Machine Learning Programs

Admissions requirements vary because machine learning specialties require knowledge in a variety of concepts. Mathematics, statistics, and computer science majors tend to excel in machine learning programs as long as they are expert programmers. Students who lack knowledge in any one of those three fields tend to require extra training to succeed.

However, many master’s programs focused on ML do not require full degrees in these specialties. For example, USC just requires a degree in a hard science major, engineering, or math. These degrees, in modern universities, frequently require programming and complex mathematical analyses. You may have to put in extra effort in learning high-level algorithms and theoretical computer science concepts. But a machine learning degree is doable with a background in hard science, engineering, or math.

Jobs for Master’s in Machine Learning Graduates

The electric vehicle industry uses machine learning extensively. For example, electric vehicles can plan travel routes to efficiently stop for charging on long-distance routes. The system has a dataset for where charging stations are located. It also has a dataset for when this particular vehicle needs to be charged. The vehicle uses the datasets together to plan the most efficient travel route and stops for charging. Electric vehicles use machine learning algorithms throughout their systems to ensure efficient operation.

Another career in machine learning involves power grid management. An efficient machine learning algorithm can predict when demand will peak and when the power supply can be scaled back. The result is more efficient power management. Computer scientists with expertise in machine learning are necessary for this career path.

Logistics and supply chain management utilize machine learning, as well. Machine learning engineers specialize in constraint-based predictive modeling. Experts are needed to account for unpredicted supply chain interruptions, such as global pandemics. Machine learning algorithms need to quickly learn from unexpected datasets and adapt the predictions and behavior for unexpected data. Expert machine learning engineers are needed to create these machine learning algorithms.

E-commerce utilizes machine learning for improving search results and targeted advertising. Computer scientists with specialties in machine learning utilize customer-specific datasets to train the machine learning algorithms to customize search results and advertisements for users.

Frequently Asked Questions About Machine Learning Degrees

How Many Master’s in Machine Learning Programs Are there?

We found 41 degrees with either a focus in Machine Learning or a professional degree in Machine Learning. About 30% of these programs are offered online. We also recommend exploring programs that combine data science and AI, which put an emphasis on machine learning skills.

Are Machine Learning Programs Hard?

Machine learning programs require advanced skills. If you have a solid foundation in discrete mathematics, statistics, and programming in languages like Python, you will find the program rewarding. Even if you do not feel prepared for these skills, you can take courses on Udemy, Coursera, and similar websites to reinforce your skills.

Further, to prepare for a master’s in ML program, speak with alumni, mentors, admissions counselors, professors, and current students for advice. Many of these people frequently comment on message boards like Discord or Reddit. In fact, many of these programs, like the Georgia Tech Online Master’s of Computer Science program, have dedicated subreddits.

Are GRE Scores Required for Admission to Master’s in ML Programs?

Most programs still require the Graduate Record Examination (GRE). However, some do not require it. Instead, they look at the applicant’s education and experience background as evidence they will succeed in the program. Some programs require foreign language assessments, such as the TOEFL, even if they do not require the GRE. To be sure, you should contact the admissions staff for requirements. Many application websites have contact forms for questions like this.