Doctor of Philosophy (PhD) in Machine Learning and PhD in Statistics degree programs both focus on analyzing data using statistical methods, but students in machine learning programs learn how to handle extremely large data sets with the use of technology. Here we discuss other similarities and differences between the programs in more detail.
Comparing a PhD in Machine Learning to a PhD in Statistics
PhD in Machine Learning
PhD in Machine Learning programs are typically interdisciplinary programs that may be offered through and/or include coursework, research, and resources from various areas, such as engineering, science, computing, biostatistics, computer science, and more. These programs train students how to gather, manage, and analyze huge collections of data with the help of technology and may take 4 to 5 years to complete. Most of these programs require students to pass comprehensive exams and complete a dissertation or doctoral thesis, and some programs may have additional requirements, such as teaching responsibilities and presenting research at an academic conference. Coursework for these degree programs vary based on a student's interests and/or the department through which the program is offered, but may include topics such as optimization, machine learning, data analysis, statistics, computing, signal processing, learning theory, and probability. Graduates with their PhD in Machine Learning are prepared for leadership positions within the field of machine learning or data science in various industries and academia and may pursue positions as professors, machine learning engineers, and more.
PhD in Statistics
PhD in Statistics programs train students in the theories and applications of statistics and usually take about 5 years to complete. These programs also require comprehensive exams and a dissertation, and some programs may require students to demonstrate mastery of a foreign language. Due to the various applications of statistics, these programs often offer interdisciplinary research opportunities with different departments or organizations. Again, coursework for these programs typically vary based on a student's interests and professional goals, but may include topics like probability, applied statistics, theoretical statistics, stochastic modeling, simulation modeling, linear programming, and operations research. Graduates are prepared to work as researchers or statisticians in industry, the government, and academia studying a wide range of subjects, such as biology, economics, sociology, computer science, and more.
Common Entrance Requirements
Admission requirements vary by institution and/or department that offers the program, especially in the case of machine learning programs, but generally, a PhD in Machine Learning and PhD in Statistics program will accept students with either a bachelor's or master's degree. Both programs typically require students to submit GRE scores, and some programs may also have a minimum GPA requirement. PhD in Machine Learning programs may consider students' research and/or work experience and may prefer students to have prior coursework in math and computer programming, while PhD in Statistics programs may require applicants to have prior coursework in areas like linear algebra, real analysis, and calculus-based statistics and probability. Applicants to either program generally need to include their transcripts, letters of recommendation, a personal statement, and/or a resume with their application.
PhD in Machine Learning and PhD in Statistics programs generally take around 5 years to complete, require a dissertation, and include coursework in statistics. Machine learning programs train students to utilize technology to analyze data and are interdisciplinary in nature, while statistics programs have a wide range of practical applications.