CS109

Course Resources
Syllabus Schedule Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Lecture Videos AIWG Student Guide
Midterm Challenge 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
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. Midterm Review 16. Central Limit Theorem 17. Sampling & Bootstrapping 18. Algorithm Analysis 19. Information Theory 20. Maximum Likelihood Estimation 21. Logistic Regression 22. Comparing Classifiers 23. Deep Learning 24. Ethics Probability 25. Reinforcement 1
Section
Section Signup Form Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8
PEP
Midterm

Schedule

Lecture 24: Ethics Probability

May 29, 2026
NVIDIA Auditorium, 1:30pm

Lecture Materials

Learning Goals

Learn about ethics