After completing this course, you will
Topics include:
Recommended: ESL - The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani and Jerome Friedman
Supplementary: ISL - An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Supplementary: SMLR - Statistical Learning and Machine Learning with R. This is my ongoing project that will include many of the course material as its book chapters.
Good knowledge of probability and linear algebra. A course which covers linear regression and uses R, such as STAT 425. Basic understanding of numerical optimization.
R and RStudio are strongly recommended for this course since all examples are provided using R. R is a freely available language and environment for statistical computing and graphics. RStudio is a free and open-source integrated development environment for R. You should have access to a computer where you are able to install the most up-to-date versions of R and RStudio, as well as install R packages.
All homework must be submitted in .pdf format. For details, please see the homework page. We expect to have about 12 sets of homework, depending on the course progression. The total score will be distributed evenly across all sets of homework. All assignments weight equally.
We will have one midterm exam. It contains approximately 15 questions. The exam is in-class and closed-book. For more details, please see the exam page.
You can form a team with up to three members to complete the final project. THere are two options: a standard project and a self-proposed project. Please see the project page for details.
| Type | Precentage |
|---|---|
| Homework | 55% |
| Midterm | 15% |
| Group Project | 30% |
Letter grades
| A+ | A | A- | B+ | B | B- | C+ | C | C- | D+ | D | D- |
|---|---|---|---|---|---|---|---|---|---|---|---|
| TBD | 93% | 90% | 87% | 83% | 80% | 77% | 73% | 70% | 67% | 63% | 60% |
For Spring 2022, lectures are in-class. Course materials will be posted on this website, and you are required to follow the course progression, especially to monitor any updates on Canvas, gradescope and this website.
The official University of Illinois policy related to academic integrity can be found in Article 1, Part 4 of the Student Code. Section 1-402 in particular outlines behavior which is considered an infraction of academic integrity. These sections of the Student Code will be upheld in this course. Any violations will be dealt with in a swift, fair and strict manner. Homework assignments are meant to be learning experiences. You may discuss the exercises with other students, but you must write up the solutions on your own. In short, do not cheat, it is not worth the risk. You are more likely to get caught than you believe. If you think you may be operating in a gray area, you most likely are.
The instructor reserves the right to make any changes he considers academically advisable. These changes, if any, will be announced in class and on the homepage of this website and Canvas. It is your responsibility to keep track of the proceedings.