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 28: The Digital Future of Probability
Dec 1st, 2021
Lecture Materials
Slides PDF
Learning Goals
Be inspired to tackle the many open problems.
Reading
None
Concept Check
None!