CS109

Course
Syllabus Course FAQ What is CS109? Honor Code Policy Course Reader Python Review Office Hours Section Locations Midterm Final CS109 Challenge
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. Variance, Bernoulli, and Binomial RVs 8. Poisson 9. Continuous 10. Gaussian 11. Probabilistic Models 12. Continuous Models 13. Inference 14. Modeling 15. General Inference 16. Beta 17. Adding 18. Central Limit Theorem 19. Bootstrapping 20. Algorithm Analysis 21. MLE: Maximum Likelihood Estimation 22. MAP: Maximum A Posteriori 23. Naive Bayes 24. Logistic Regression 25. Ethics 26. Deep Learning 27. Deep Learning II 28. Future
Section
Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Section 7 Section 8 Section 9

Schedule

Lecture 16: Beta

Oct 27th, 2021

Lecture Materials

Learning Goals

Know how to think about uncertainty in probabilities

Reading

Concept Check

None