A master’s degree in machine learning provides the necessary training and background for students to launch a successful career in the exciting and growing field of machine learning.
This guide contains a comprehensive background on the latest machine learning master’s programs, online machine learning master’s programs, and what to expect from the curriculum.
Why Pursue a Master’s Degree in Machine Learning?
Machine learning is the study of computer algorithms that improve automatically using data and prior experiences. Machine learning allows a program to build a model for decision-making based on training data. Using feedback from trial and error, these computer programs can develop the ability to make decisions for themselves without external input.
Machine learning has practical applications in many different fields. Computers are much better at processing large amounts of data and may identify patterns faster than people can. This is an invaluable skill in the healthcare, transportation, finance, agriculture, and cybersecurity industries, where making accurate predictions and data-driven decisions are vital to profitability. Machine learning can also reduce operating costs in manufacturing, marketing, retail, transcription, and software development companies as more analysis becomes dependent on computer processing.
The growing demand for machine learning specialists and data scientists makes a master’s degree in machine learning a good investment. Many top firms are onboarding qualified professionals with machine learning skill sets as quickly as they can find them. Candidates who hold an advanced degree in the field can earn higher compensation and qualify for a greater number of roles.
What Does a Master’s in Machine Learning Program Look Like?
A master’s degree program in machine learning provides a student with advanced skills in solving a specific task with high-level algorithms. Students who understand the theory and applications of machine learning will be able to apply their knowledge to a wide range of potential problems. Students will learn to overcome major challenges in machine learning, such as poor quality of data, underfitting or overfitting of training data, lack of training data, slow implementation, and imperfections in the algorithm as data grows.
Like most master’s programs, a graduate degree in machine learning will take about two years for a full-time student to complete. Some of these courses may include:
- Artificial intelligence
- Computation learning
- Algorithm design and analysis
- Software engineering
- Computer vision
- Intelligent interaction
- Fuzzy systems
- Stochastic processes
- Artificial neural networks
- Applied optimization
Machine learning can also be offered as a concentration to a master’s degree in artificial learning. In this type of offering, the student is typically required to complete at least four core courses and three concentration courses within machine learning. Other concentrations include computer vision, intelligent interaction, and knowledge management or reasoning.
Master’s degree programs typically provide a project option to allow students greater hands-on experience within their educational studies. The project option may require a fully-developed report describing the results of an independent study project under the supervision of an advisor. Project options typically require the completion of foundation courses, concentration courses, elective courses, and the master’s project itself.
Many forms of hands-on experience are available while students obtain their master’s degrees in machine learning. This may be in the form of an independent research project or capstone project, as previously outlined. However, internships and co-ops can be just as effective in building a machine learning student’s portfolio and adapting the student’s theoretical knowledge to real-world applications.
Master’s in Machine Learning Online
Online master’s degrees in machine learning are essentially the same as their on-campus counterparts. Students need to meet the same admissions requirements and take the same courses to achieve their degrees.
The main difference between online and on-campus master’s degrees in machine learning is the delivery of the course content. Instead of sitting in the classroom, online students will take their courses over the internet. The online option has a few advantages over an in-person degree program:
- Geographic flexibility: With an on-campus master’s degree, it’s necessary to live at or near the school you’re attending. With an online degree program, you can take classes from anywhere. This makes it possible to take classes at a higher-quality school or one in a more expensive area without the cost of moving and living there.
- Lower costs: Online degree programs are often less expensive than in-person counterparts. This can reduce both tuition and the cost of living.
- Flexible schedules: Many online master’s degree courses offer asynchronous courses with recorded lectures and may only require attendance at scheduled times for exams or office hours. This makes it easier to attend these programs while maintaining a job or handling family commitments.
Machine Learning Degree Requirements
A master’s degree in machine learning is highly technical and theoretical. The program’s goal is to help students learn to apply existing knowledge and skills to machine learning problems. Some skills that students are likely to have going into the program include:
- Probability and Statistics: Machine learning is based on applied probability and statistics. Students need to be proficient in these mathematical fields to learn to use this knowledge as part of their degree program.
In addition to certain knowledge, making it through the application process for a master’s in machine learning will likely require the following:
- Bachelor’s degree: Most MS in machine learning programs require an undergraduate degree in Computer Science, Mathematics, or a related field. This demonstrates that students have the fundamental theoretical knowledge necessary to succeed in the program.
- GRE or GMAT: Many schools require applicants to take the Graduate Record Examinations (GRE) or Graduate Management Admission Test (GMAT) with a specific minimum score for a graduate degree program.
- Transcripts: These are commonly required as part of the admissions process to prove that an applicant holds an undergraduate degree and has completed any necessary prerequisites.
- References: Master’s programs commonly require references from instructors or other professionals familiar with the applicant’s knowledge and work.
The Cost of a Master’s Degree in Machine Learning
Tuition for a master’s degree in machine learning can vary greatly depending on the school. Tuition is impacted by many different factors, including the quality of the school and degree program, whether the student is pursuing the degree online or in-person, and geographic locations.
When pursuing a master’s in machine learning, a student doesn’t necessarily have to pay for the full tuition. Some options for financial assistance include:
- Financial Aid: Many schools offer financial aid to qualified students. This can offset some or all of the cost of a master’s degree program.
- Scholarships and Fellowships: Students may also be eligible for scholarships and fellowships based on academic performance and demonstrable need, in addition to need-based financial aid.
- Assistantships: Many schools offer graduate students the option to offset their tuition with a teaching assistantship. A graduate assistant may help a professor teach their classes or participate in their research, which helps to pay for schooling and provides valuable experience.
- Corporate Programs: Machine learning is a high-demand technical skill across many industries. This means that a student’s employer may be willing to cover some or all higher education costs to develop the knowledge and experience the business needs.
Career Paths with a Master’s in Machine Learning
With a master’s in machine learning, a student can pursue several different job roles in information science. Some common job titles include:
- Artificial intelligence specialist
- Machine learning engineer
- Data science specialist
- Machine learning researcher
- Machine learning specialist
According to Payscale, a degree holder of a master’s in machine learning should expect to earn an average of $102,906. Payscale adds that the average machine learning engineer’s salary is a base of $112,266/year. At the same time, the Bureau of Labor Statistics claims that the median pay for computer and information research scientists is $126,830. These salaries are significantly higher than the average salary of a BS in Computer Science (a common entry point into the degree), which is $86,675. The salary difference and the cost of a degree mean that an MS in machine learning can break even in less than two years.
In addition to a salary increase, holders of an MS in machine learning can look forward to entering a rapidly-growing job market. The BLS anticipates a 15 percent growth in Computer and Information Research Science, significantly faster than the average field. Additionally, students with a solid grounding in machine learning can likely expect to be recruited quickly by the top tech companies that need machine learning talent.
Master’s in Machine Learning FAQs
Employers not only demand expertise in a domain; they also require proof of qualifications. Therefore, having a practical and theoretical knowledge of machine learning and neural networks makes a candidate very competitive. There is also high academic competition within each program. Some students may find the mathematics of machine learning theory more challenging than their full-time jobs, with backpropagation and partial derivatives typically required in a machine learning curriculum. Enrolled students may need to increase their concentration, focus, and attention levels to remain competitive in their machine learning coursework and complete the master’s program with high marks.
A master’s degree in machine learning opens many career paths that would otherwise be unavailable. Another key consideration is earning potential. Someone who holds a master’s degree in machine learning (versus someone with a bachelor’s in computer science) can expect to earn an increased salary. It is important to calculate the return of any investment before allocating significant funds, and pursuing a master’s in machine learning can be profitable for the right candidates.
A master’s in machine learning program is designed to provide students with the theoretical knowledge and hands-on experience necessary to apply ML concepts effectively. With this degree, a student can pursue careers in machine learning or data science in industries like customer support, predictive maintenance, industrial automation, software forecasting, and medical diagnosis.
Online master’s programs in machine learning are very common. Southern New Hampshire University has over 60,000 online students, with most of their students registering online rather than in-person. Colorado State University’s Global Campus, Drexel University, and Sophia University also offer an online master’s program in ML.
The best way to prepare is to ensure that the fundamentals of mathematics, computer science, and programming are well-understood. It helps to be proficient in one or more programming languages, including Python, R. Java, C++, Julia, and/or LISP. Kevin P. Murphy’s book, Machine Learning: A Probabilistic Perspective, is recommended for students who want to develop their understanding of machine learning fully. Finally, students who enroll in a master’s degree program in machine learning will be expected to write essays and reports. Machine learning students will need to state the goals of their research, specify the performance and learning tasks that they wish to achieve, describe the representation and organization of the system’s knowledge, explain the performance and learning components of the machine learning system, and evaluate the approach to machine learning by including evidence to support their claims. Machine learning authors will be expected to relate their selected approach to other methods and disclose the approach’s limitations, suggesting directions for future research. Covering each of these components when submitting machine learning reports will ensure high-quality papers and strong performance for a master’s program in machine learning.