Statistics and Machine Learning Major

This is a joint major between Dietrich and the School of Computer Science.

This joint major develops the critical ideas and skills underlying statistical machine learning — the creation and study of algorithms that enable systems to automatically learn and improve with experience. It is ideal for students interested in statistical computation, data science, or “Big Data” problems, including those planning to pursue a related Ph.D. or a job in the tech industry.

Major Requirements

Theory Requirements
Course Topic/Title Course Number Units Prerequisites
Calculus 21-111 and 112, or 21-120 20 or 10
Integration and Approximation 21-122 10 21-112 or 21-120
Multivariate Calc/Analysis 21-256, 21-259, or 21-268  9–10 21-112 or 21-120
Concepts of Mathematics 21-127 10
Linear/Matrix Algebra 21-240, 21-241, or 21-242 10
Probability 36-217, 21-325, 15-359, or 36-225  9 21-112, 21-122, 21-123, 21-256, or 21-259
Statistical Inference 36-226 or 36-326  9 C or higher in 36-217, 36-225, 21-325, or 15-359
Data-Analysis Requirements (Option 1)
Course Topic/Title Course Number Units Prerequisites
Beginning Data Analysis 36-201, 36-220, or 36-247  9
Intermediate Data Analysis 36-202, 36-208, or 36-309  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Modern Regression 36-401  9 C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241
Advanced Methods for Data Analysis 36-402  9 C or higher in 36-401
Data-Analysis Requirements (Option 2)
Course Topic/Title Course Number Units Prerequisites
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Advanced Elective 36-315, 36-303, 36-490, or 36-46x  9 various
Modern Regression 36-401  9 C or higher in 36-226, 36-326, or 36-625 and pass 21-240 or 21-241
Advanced Methods for Data Analysis 36-402  9 C or higher in 36-401
Computing Requirements
Course Topic/Title Course Number Units Prerequisites
Statistical Computing 36-350 or 36-650/750  9 36-202, 36-208, 36-309, 70-208, or equivalent
Fundamentals of Programming 15-112 12
Principles of Iterative Computation 15-122 10 C or higher in 15-112
Machine Learning 10-401/601/701 12 C or higher in (15-122 or 15-123) and (15-151 or 21-127) and (36-217 or 36-225 or 21-325 or 15-359)
Algorithms and Advanced Data Structures 15-351/451 12 15-111, 15-123, 15-121, or 15-122
Large Data Sets 10-405/605 or Advanced
Machine Learning Elective
(10-605, 15-381, 15-386, 16-720, 16-311, 11-411, or 11-761)
*Additional courses may count, please see a Statistics Advisor for approval
** Independent research with an ML faculty member
 9 vary by elective