Program in Statistics and Machine Learning

Academic Unit

Program Information

INFORMATION AND DEPARTMENTAL PLAN OF STUDY

The Program in Statistics and Machine Learning is offered by the Center for Statistics and Machine Learning. The program is designed for students, concentrating in any department, who have a strong interest in data analysis and its application across disciplines. Statistics and machine learning -- the academic disciplines centered around developing and understanding data analysis tools -- play an essential role in various scientific fields including biology, engineering, and the social sciences. This new field of "data science" is interdisciplinary, merging contributions from computer science and statistics, and addressing numerous applied problems. Examples of data analysis problems include analyzing massive quantities of text and images, modeling cell-biological processes, pricing financial assets, evaluating the efficacy of public policy programs, and forecasting election outcomes. In addition to its importance in scientific research and policy making, the study of data analysis comes with its own theoretical challenges, such as the development of methods and algorithms for making reliable inferences from high-dimensional and heterogeneous data. This program provides students with a set of tools required for addressing these emerging challenges. Through the program, students will learn basic theoretical frameworks and apply statistics and machine learning methods to many problems of interest.

ADMISSION TO THE PROGRAM

Students are admitted to the program after they have chosen a concentration, generally by the beginning of their junior year. At that time, students must have prepared a tentative plan and timeline for completing all of the requirements of the program, including required courses and independent work (as outlined below), as well as any prerequisites for the selected courses.

For enrollment, please use this form: Certificate Enrollment Application(link is external)

For questions, contact us at smlcert@princeton.edu

PROGRAM OF STUDY

Students are required to take a total of five courses and earn at least B- for each course: one of the “Foundations of Statistics” courses, one of the “Foundations of Machine Learning” courses, and three elective courses. With all necessary permissions, advanced students may also take approved graduate-level courses. Students may count at most two courses from another degree program (departmental concentration) towards this certificate program.

Students are also required to complete a thesis or at least one semester of independent work in their junior or senior year on a topic that makes substantial application or study of machine learning or statistics. In situations where this is not feasible, students should consult with the program director to discuss alternate arrangements.  This work may be used to satisfy the requirements of both the program and the student's department of concentration. Submission is due on the same date as your department deadline for thesis or junior independent work. All work will be reviewed by the Statistics and Machine Learning Certificate committee. At the end of each year, there will be a public poster session at which students are required to present their work to each other, to other students, and to the faculty.

Finally, students are encouraged to attend the statistics and machine learning colloquia on campus, including the CSML Seminar Series.

Courses

Foundations of Statistics - one of the following courses:

ECO 202 Statistics & Data Analysis for Economics
EEB/MOL 355 Introduction to Statistics for Biology
ORF 245 Fundamentals of Statistics
POL 345/SOC 305 Intro to Quantitative Social Science
PSY 251 Quantitative Methods
WWS 200 Statistics for Social Science

Foundations of Machine Learning - one of the following courses:

COS 324 Introduction to Machine Learning
COS 424/SML 302 Fundamentals of Machine Learning
ELE 364 – Machine Learning for Predictive Data Analytics
ORF 350 Analysis of Big Data

Three of the following courses (including those above, with permission):

Data Science
SML 201 Introduction to Data Science

Machine Learning
COS 402 Machine Learning and ArtificiaI Intelligence
COS 429 Computer Vision
COS 495 Neural Networks –Theory and Applications
ELE 477 Kernel-Based Machine Learning
ELE 488 Image Processing
ORF 418 Optimal Learning

Theory
MAT 385 Probability Theory
ORF 309 Probability and Stochastic Systems
ORF 363 Computing and Optimization

Applications
AST 303 Observing and Modeling the Universe
CEE 460 Risk Analysis
ECO 302 Econometrics
ECO 312 Econometrics: A Mathematical Approach
ECO 313 Econometric Applications
ELE 480/NEU 480/PSY 480 fMRI Decoding: Reading Minds Using Brain Scans
GEO 422 Data, Models, and Uncertainty in the Natural Sciences
ORF 405 Regression and Applied Time Series
POL 346 Applied Quantitative Analysis
QCB 408 Foundations of Applied Statistics and Data Science
SOC 400 Applied Social Statistics

Example Paths for the SML Certificate

Computer Science, Mathematics, or Engineering Student

ORF 245 Fundamentals of Engineering Statistics
COS 424 Interacting with Data
ORF 309 Probability and Stochastic Systems
ORF 350 Analysis of Big Data
COS 402 Artificial Intelligence

Economics or Finance Student

ORF 245 Fundamentals of Engineering Statistics
COS 424 Interacting with Data
ECO 312 Econometrics: A Mathematical Approach
ORF 350 Analysis of Big Data
ECO 313 Econometrics Applications

Life Sciences Student

MOL 355 Introduction to Statistics for Biology
COS 424 Interacting with Data
ORF 309 Probability and Stochastic Systems
GEO 422 Data, Models, and Uncertainty in the Natural Sciences
MOL 436 Statistical Methods for Genomic Data

Social Sciences Student

POL 345 Quantitative Analysis and Politics
COS 424 Interacting with Data
ECO 312 Econometrics: A Mathematical Approach
ECO 313 Econometric Applications
POL 346 Applied Quantitative Analysis

CERTIFICATE OF PROFICIENCY

Students who fulfill the program requirements receive a certificate upon graduation.