The graduate certificate is comprised of three components: (a) completion of three appropriate graduate courses, (b) a relevant research contribution, and (c) a research seminar. More details on each of these are below. If you have additional questions, please contact email@example.com
Take for credit and receive an average GPA of B+ (3.3) or better in three courses from the approved list that has three categories: core machine learning, core statistics and probabilistic modeling, and electives. One course must be selected from each category. With the permission of the certificate director, the elective course can be selected from a core category provided it does not significantly overlap with the other course selected from that category. At least one of the three courses must be outside the student’s home department and at most one course can be below the 500 level.
The core curriculum is intended to provide training in the foundations of statistics and machine learning while ensuring that certificate students have some breadth across the core of statistics and machine learning. A list of approved core courses in the two areas is included below. In addition, a certificate student selects the third course from a listed set of elective courses that expands on the core courses. These electives delve more deeply into supporting material (e.g., optimization) or focus on applications in a specific domain.
Students may not count courses that are used to satisfy core requirements in their home department concentration toward this certificate, however they may count up to two electives that were taken for their degree requirements.
Please note: all coursework, including SML 510 must be completed before a student enters into DCE status.
For students completing a thesis or dissertation as part of their degree, the thesis or dissertation (or a publishable research paper) should include a significant component making contributions to statistics or machine learning, or rigorous use of such methods in an application domain. See below for additional details. For non-thesis master’s degree students, this requirement can be satisfied by a technical presentation on a topic relevant to the program.
To ensure that an important component of a Ph.D. student’s dissertation involves either rigorous data analysis, and/or mathematical or computational modeling of data or machine learning problems, one of the dissertation readers or FPO committee members must be a participating graduate certificate faculty member (see list below). This reader will be required to either send a letter, or their reader’s report, to the program director to verify that the dissertation satisfies this requirement. Master’s students who complete a thesis follow the same requirement. For non-thesis master’s students, their technical presentation will be reviewed by the certificate director.
Graduate Research Seminar
Prior to graduation, students must enroll in and complete the requirements of the CSML graduate seminar series (SML 510) for at least one semester.
The CSML graduate seminar, SML 510 serves as a venue for discussing current methods and results and the integration of different research approaches to data analysis. Attendance and participation in the CSML graduate seminar for at least one semester is required. It helps teach students how to communicate technical ideas to a broad audience and encourages the development of skills for interacting with other students, postdoctoral fellows, and faculty who are investigating data analysis problems. It also serves to build a supporting community of young scholars with shared interests.
Core Machine Learning – one of the following courses
- COS 402 Machine Learning and Artificial Intelligence
- COS 424 / COS 524 Fundamentals of Machine Learning
- COS 485 Neural Networks: Theory and Applications
- COS 511 Theoretical Machine Learning
- ELE 535 Machine Learning and Pattern Recognition
Core Statistics and Probabilistic Modeling – one of the following courses
- COS 513 Foundations of Probabilistic Modeling
- ECO 513 Time Series Econometrics
- ECO 519 Advanced Econometrics: Nonlinear Models
- ELE 530 Estimation and Detection
- ORF 524 Statistical Theory and Methods
- POL 572 Quantitative Analysis II
- QCB 508 Foundations of Statistical Genomics
- SML 505 Modern Statistics
Electives – one of the following courses (including those above, with permission)
- APC 527 Random Graphs and Networks
- APC/ORF 550 - Topics in Probability - Probability in High Dimension
- COS 534 - Fairness in Machine Learning
- ECO 515 Econometric Modeling
- ELE 538B Sparsity, Structure, and Inference
- ELE 522 Large-Scale Optimization for Data Science
- FIN 580 Quantitative Data Analysis in Finance
- MAT585/APC520 Mathematical Analysis of Massive Data Sets
- NEU 560 - Statistical Modeling and Analysis of Neural Data
- ORF 505 Statistical Analysis of Financial Data
- ORF 522 Linear and Nonlinear Optimization
- ORF 523 Convex and Conic Optimization
- ORF 525 Statistical Learning & Nonparametric Estimation
- ORF 526 Probability Theory
- POL 573 Quantitative Analysis III
- POL 574 Quantitative Analysis IV
- POP 507 Generalized Linear Statistical Models
- SOC 504 Advanced Social Statistics