Machine Learning: Master's Vs. PhD

Jun 11, 2019

There are some key differences between master's and doctoral degree programs in the field of machine learning, such as format, length, and research requirements. Explore some of the similarities and differences between the degree levels here.

Students interested in working with huge amounts of data, performing data collection, and studying computerization, may be interested in pursuing a graduate degree in machine learning. These programs are available at the master's and doctoral levels and may include similar course topics, but often have different graduation requirements. Find out more about each degree level to see which may be right for you.

Comparing a Master's to a PhD in Machine Learning

Master's in Machine Learning

Master's degree programs in machine learning are most commonly offered as Master of Science (MS) degrees in machine learning, but may also be paired with subjects like signal processing or be offered as MS degrees in areas like computer science with a specialization in machine learning. Some of these degree programs may be available in online formats, can be taken full- or part-time, and some accelerated programs may take as little as 1 year to complete, but in general, students complete the program in about 2 years. Most of these programs include a practicum experience during the summer for hands-on learning or other internship experiences for real-world applications. These programs usually require around 30 credits and some programs may offer a thesis or non-thesis option, but some common course topics for these degree programs include statistical machine learning, artificial intelligence, data analysis, probability, optimization, and deep learning. Graduates with their master's in machine learning may pursue careers in the technology industry, research, or academia.

PhD in Machine Learning

At the doctoral level, students can typically pursue their Doctor of Philosophy (PhD) in Machine Learning or other related areas, like a PhD in Statistics with a track in machine learning and big data. These are typically on-campus programs that require comprehensive exams, a doctoral thesis or dissertation, and may have additional teaching, research, and/or conference presentation requirements. Most of these programs take 4 to 5 years to complete, are usually taken full-time, and students typically complete the majority of their coursework by the 3rd year of the program to begin their dissertation research. Students may be required to have a minor and coursework at the doctoral level is a little more flexible to allow students to pursue their individual interests, but may include topics in machine learning, statistical machine learning, data analysis, statistics, optimization, and data models. Graduates of PhD in Machine Learning programs usually pursue research-based careers in the field.

Common Entrance Requirements

Applicants to master's degree programs in machine learning must hold a bachelor's degree and may need to meet a minimum GPA around a 3.0, while those applying to doctoral programs can usually hold a Bachelor of Science (BS) or MS degree. Some doctoral programs may prefer applicants with relevant research experience in the field and/or any independent research projects the applicant completed. Depending on how the program is offered, doctoral students may be able to apply through one of several schools or departments, such as computing, math, or engineering. The majority of these degree programs at the master's and doctoral levels require applicants to take the GRE. Other common application materials may include transcripts, letters of recommendation, a resume, and/or a statement of purpose.

MS in Machine Learning degree programs are typically shorter and may require more hands-on training experiences than PhD programs in the field. PhD students typically focus on research and are usually required to complete a dissertation.

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