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. 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 one of the Statistics and Machine Learning colloquia on campus, including the CSML Seminar Series.

Courses

Fundamentals 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

Fundamentals of Machine Learning - one of the following courses

  • 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
  • POL 245 Visualizing Data
  • SML 201 Introduction to Data Science
Machine Learning
  • COS 402 Machine Learning and AI
  • COS 429 Computer Vision
  • COS 495 Neural Networks –Theory and Applications
  • ELE 477 Kernel-Based Machine Learning
  • 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

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