CS109
Course
Syllabus
Course FAQ
What is CS109?
Honor Code Policy
Staff / Office Hours
Problem Sets
1. Counting
2. Core Probability
3. Random Variables
4. Joint Variables
5. The Penultimate
6. Machine Learning
Lecture
1. Welcome
2. Combinatorics
3. Probability
4. Conditioning and Bayes
5. Independence
6. Conditional Independence and Random Variables
7. Variance, Bernoulli, and Binomial RVs
8. Poisson, Geometric, and Negative Binomial RVs
9. Continuous
10. Gaussian
11. Probabilistic Models
12. Independent Random Variables
13. Joint Statistics
14. Conditional Random Variables
15. Continuous Probabilistic Models
16. Continuous Inference
17. General Inference and Bayesian Networks
18. Central Limit Theorem
19. Sample Statistics and Bootstrapping
20. Beta
21. MLE: Maximum Likelihood Estimation
22. MAP: Maximum A Posteriori
23. Naive Bayes
24. Logistic Regression
25. Deep Learning
26. Ethics Of Machine Learning
27. Future Of Probability
Section
Section 1
Section 2
Section 3
Section 4
Section 5
Section 6
Section 7
Section 8
Section 9
Resources
Course Reader Draft
Calculation Reference
Probability Reference (overleaf)
Probability in Python
Latex in CS109
Latex Cheat Sheet
Challenge
Galton Board
Gaussian Calculator
ELO Ratings
Mini-WebMD
Sampling Distributions
Central Limit Theorem
MLE Uniform
Handwritten Digit Classification
DRE and Atari-Like Games
Quizzes
Quizzes Overview
Quiz 1
Quiz 2
Quiz 3
Schedule
Quiz 3: Parameter Estimation and Machine Learning
Mar 17th to Mar 19th, 2021
The Quiz 3 PDF file has your exam! You can optionally use the latex file. Make sure to submit
before
4:59am on Saturday March 20th, which is Friday, March 19th at 11:59pm AOE.
Quiz Materials
Quiz PDF
Solution
Latex (optional)
(optional) HTML