CS109

Course Resources
Syllabus Honor Code Office Hours Course Reader Python Review Latex Cheat Sheet Fall 2022 Videos ACE Practice
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. General Inference 15. Beta 16. Adding Variables 17. Sampling Statistics 18. Bootstrapping 19. Algorithm Analysis 20. Maximum Likelihood Estimation 21. Maximum A Posteriori 22. Naive Bayes 23. Logistic Regression 24. Deep Learning 25. Ethics 26. DallE and GPT
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8

Schedule

Lecture 28: Your Future Of Probability

Dec 7th, 2022
Hewlett 200, 3:30p

Lecture Materials

Learning Goals

To gain a sense of what the future of probability-driven AI might look like!

Reading

None