CS109

Course
Syllabus Honor Code Office Hours
Midterm Final
Problem Sets
1. Counting 2. Core Probability 3. Random Variables 4. Probabilistic Models 5. Uncertainty Theory 6. MLE 7. Extra: 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. Naive Bayes 23. Logistic Regression
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7
Resources
Course Reader Python Review Latex Cheat Sheet

Schedule

Lecture 22: Naive Bayes

Aug 14th, 2023

Lecture Materials

Learning Goals

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

Reading