CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos AIWG Student Guide
Midterm 1 Challenge
Problem Sets
1. Core Probability 2. Discrete Random Variables 3. Continuous Random Variables 4. Probabilistic Models 5. Uncertainty Theory
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

Schedule

Lecture 27: Your Future Of Probability

June 3rd, 2026
CoDa B80, 10:30am

Lecture Materials

Learning Goals

To gain a sense of what the future of probability might look like for you