CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos AIWG Student Guide
Challenge Midterm Final
Problem Sets
1. Core Probability 2. Discrete Random Variables 3. Continuous Random Variables 4. Probabilistic Models 5. Uncertainty Theory 6. Information Theory + MLE 7. Machine Learning
Music Voting View Playlist
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. Algorithm Analysis 17. Information Theory 18. Sampling & Bootstrapping 19. Maximum Likelihood Estimation 20. Logistic Regression 21. Comparing Classifiers 22. Deep Learning 23. Reinforcement 1 24. Reinforcement 2 25. Diffusion 26. Future
Check Lecture Attendance
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8
Section Reassign Form Times and Locations
PEP
Midterm Final

Schedule

Lecture 26: Future

Mar 11th, 2026
Hewlett 200, 3p

Lecture Materials

Learning Goals

Learn about how you can prepare for the future beyond CS109.