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. Central Limit Theorem 19. Bootstrapping 20. Algorithm Analysis 21. Maximum Likelihood Estimation 22. Maximum A Posteriori 23. Naive Bayes 24. Logistic Regression 25. Ethics 26. Deep Learning
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8 Section 9
Resources
Course Reader Python Review Latex Cheat Sheet

Schedule

Lecture 23: Naive Bayes

March 2nd, 2022

Lecture Materials

Learning Goals

Be able to implement and use Naive Bayes. Understand why it works and what it is doing.

Reading