CS109
Course
Syllabus
Honor Code
Office Hours
Challenge
Midterm
Final
Problem Sets
1. Counting
2. Core Probability
3. Random Variables
4. Probabilistic Models
5. Uncertainty Theory
6. Machine Learning
Lecture
1. Welcome
2. Combinatorics
3. Probability
4. Conditioning and Bayes
5. Independence
6. Random Variables
7. Binomial
8. Poisson
9. Continuous
10. Gaussian
11. Probabilistic Models
12. Inference
13. Inference Part 2
14. Modeling
15. General Inference
16. Beta
17. Adding Variables
18. Central Limit Theorem
19. Bootstrapping
20. Algorithm Analysis
21. Maximum Likelihood Estimation
22. Maximum A Posteriori
23. Naive Bayes
24. Logistic Regression
25. Ethics
26. Deep Learning
Section
Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section 7
Section 8
Section 9
Resources
Course Reader
Python Review
Latex Cheat Sheet
Schedule
Lecture 16: Beta
Feb 11th, 2022
Lecture Materials
Slides PDF
Live Zoom Link
Learning Goals
Know how to think about uncertainty in probabilities
Reading
Beta