This page gives an informal overview of typical classes taken by PhD graduate students in the department. - If you are interested in interdisciplinary work, you may wish to include a course from another department.
- If you have done graduate work at another university, the material in the "A" courses may already be familiar to you (or it may not.) Other courses may be more appropriate.
- You may come in to the program knowing that you wish to specialize in probability. After discussion with your graduate advisor it may be possible not to take any of 210A/B or 215A/B.
However, you will need to consult with the graduate advisor to receive approval.
Each course has its own assessment. Depending on the course, this may include problem sets, group projects, take-home and/or in-class midterms, and final exams. At the end of your first year, the Graduate Faculty Advisor will evaluate your coursework as a whole and decide whether you advance to the next stage of your graduate career.
STAT 243 is an intensive course, designed to help students pick up some relevant programming skills. If you don't feel comfortable programming, you should consider taking this. In an applied course like STAT 215, there is a large computing component, and though the course GSI will give an introduction to the software required, students with limited statistical computing experience often find it difficult to pick up the software skills at the same time as the course material.
Rigorous and theoretical; a suitable course if your mathematical background is strong. Some knowledge of measure theory is extremely helpful: the basics of measure theory are covered in the first few weeks of lectures, but students who haven't seen it before often find this isn't enough. The homeworks given in this course are time-consuming and hard. Students often form study groups to share ideas. 205A: Measure theoretical approach to probability, conditional expectation, martingales. 205B: Markov processes, limit theorems, characteristic functions, ergodic theory, brownian motion.
215A: Exploratory techniques; critical readings of applied papers; overview of methods, including regression, testing, and resampling. 215B: Topics include advanced regression, causal inference, and optimization.
STAT 238 Bayesian Statistics STAT C239A and C239B The Statistics of Causal Inference in the Social Science STAT 240 Nonparametric and Robust Methods STAT 241A Statistical Learning Theory STAT 241B Advanced Topics in Learning and Decision Making STAT 243 Introduction to Statistical Computing STAT 244 Statistical Computing STAT 246 Statistical Genetics STAT 248 Analysis of Time Series STAT 251 Stochastic Analysis with Applications to Mathematical Finance STAT 259 Reproducible and Collaborative Statistical Data Science STAT 261 Quantitative/Statistical Research Methods in Social Sciences STAT 272 Statistical Consulting
These courses are not taught regularly, and the content varies from semester to semester and by instructor. Topic announcements are usually made at the end of the preceding semester. These courses can be taken repeatedly for credit by graduate students, and the department encourages people to take these for credit in order to show the university that there really are people taking these courses. The three advanced topics course titles are: - Stat 206: Stochastic Processes
- Stat 212: Topics in Theoretical Statistics
- Stat 260: Topics in Probability and Statistics
- Interacting Diffusions in Probability and Statistical Physics (Stat 206, Fall 2016, Hammond)
- Self-avoiding Walks (Stat 206, Spring 2016, Hammond)
- Topics on Deep Learning and High-dimensional Representation Learning (Stat 212, Spring 2016, Bruna)
- Convex Optimization and Approximation: Optimization for Modern Data Analysis (Stat 260, Spring 2016, Recht)
In addition, various individual study courses may be taken under the supervision of a faculty member, and some larger scale reading groups can be organized. For example, in Fall 2016 there's a reading group in modern causal inference in complex models.
The department runs many seminars. The main one is the Neyman seminar, from 4-5pm on Wednesdays in Evans 1011. Students are encouraged to enroll for credit in these seminars, particularly the Neyman and Probability seminars. Seminars are an important part of life in the department. They allow you to see how new theory is developed, and how existing theory is put into action. They also provide research topics and ideas, as well as sometimes providing delicious food. |