CS109
Course Resources
Syllabus
Schedule
Honor Code
Office Hours
Course Reader
Python Review
Latex Cheat Sheet
Lecture Videos
AIWG Student Guide
Midterm 1
Midterm 2
Challenge
Problem Sets
1. Core Probability
2. Discrete Random Variables
3. Continuous Random Variables
4. Probabilistic Models
5. Uncertainty Theory
6. Machine Learning and Logistic Regression
Lecture
1. Welcome
2. Conditioning and Bayes
3. Independence
4. Counting
5. Binomial
6. Moments
7. Poisson
8. Continuous
9. Gaussian
10. Probabilistic Models
11. Inference
12. General Inference
13. Multinomial
14. Beta
15. Central Limit Theorem
16. Sampling & Bootstrapping
17. Algorithm Analysis
18. Information Theory
19. Maximum Likelihood Estimation
20. Logistic Regression
21. Comparing Classifiers
22. Deep Learning
Schedule
Lecture 14: Beta
July 15, 2026
CoDa B80, 10:30am
Lecture Materials
Slides
LLM Questions
Lecture Questions
Lecture Solutions
Lecture Code
Reading
Learning Goals
Know how to think about uncertainty in probabilities