Lectures have two formats: whiteboard presentation and slides presentation.
Attending lectures is absolutely critical.
Slides and lecture notes will be made available after lectures, but they will be difficult to understand in isolation. Readings will cover some---but not all---of the course material.
Most lectures are paired with a reading. These are optional and classes will not exactly follow the readings, but you will get more out of the lectures if you skim the readings afterwards. Readings will be drawn from these books:
- UML: Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David
- MLaPP: Machine Learning: a Probabilistic Perspective by Kevin Patrick Murphy
- ESL: The Elements of Statistical Learning: by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
You are welcome to buy physical copies if you wish---they're good books!---but the online versions will suffice for this course.
Each homework will have two parts: a programming component and a written component.
There will be 5 homeworks in total, issued roughly every two weeks. Homeworks will generally be posted after lecture on Thursday, and are due roughly two weeks later before lecture. Keep an eye on the calendar!
Late homework will not be accepted---we will not have a late day policy. Instead, your lowest homework score will be dropped. Limited partial credit is possible for partial homework solutions.
Collaboration policy: you can discuss with other students about the homework, but you must write up and code up the solutions on your own! You also must mention the names of the students you discuss with.
Midterm and Final exam
- Due to COVID-19, there will be no midterm exam. We will have a take-home final exam.
Your grade will be based on these components:
- Homeworks: 72%
- Final Exam: 18%
- Participation: 10%
Your lowest homework score will be dropped.