Work in progress: Chris Piech has been putting together his notes into a course reader format. He is in the early stages of the project so you are looking at a rough draft. You are not responsible for material covered in the course reader that is not in the lectures/lecture notes. However, you are responsible for material in the lecture notes that is not in the course reader draft.

NOTE: You should be using the lecture notes for this course, listed on the front page. The below course reader is provided as a preview of what topics are to come, in case you are interested.

Probability for Computer Scientists

From Counting to Deep Learning

1. Counting

2. Probability

3. Conditional Probability

4. Random Variables

5. Discrete Distributions

6. Continuous Distributions

7. Multivariate

8. Probability as a Variable

9. Sampling

10. Central Theorems

11. Parameter Estimation

12. Machine Learning