CS109

Course
Syllabus Honor Code Office Hours
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. Modeling 15. General Inference 16. Beta 17. Adding Variables 18. Sampling Statistics 19. Bootstrapping 20. Algorithm Analysis 21. Maximum Likelihood Estimation 22. Maximum A Posteriori 23. Naive Bayes 24. Logistic Regression 25. Deep Learning 26. Ethics 27. DallE and GPT 28. Your Future
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8 Section 9
Reassignment Form
Resources
Course Reader Python Review Latex Cheat Sheet Video Recordings CS109A Resources

Schedule

Section 7

Nov 16th, 2022

This week we'll get practice with Algorithmic Analysis and Maximum Likelihood Estimation.

Section Materials

Section Handout