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CS109
Lecture Notes
2 - Counting
3 - Calculation Ref
5 - Combinatorics
6 - Probability
7 - Conditional Probability
8 - Independence
10 - Random Variables
11 - Variance
12 - Poisson and More
13 - Continuous RV
16 - Gaussian
17 - Standard Normal
18 - Joint Distributions
19 - More Joint Properties
20 - Convolution and Conditional
22 - Beta Distributions
27 - Variance From Events
28 - Covariance and Samples
29 - Correlation
30 - Conditional Expectation
31 - Central Theorems
34 - Parameter Estimation
35 - Maximum Likelihood
37 - Maximum A Posteriori
39 - Naive Bayes
40 - Logistic Regression
41 - Deep Learning
Problem Sets
Problem Set 1
Problem Set 2
Problem Set 3
Problem Set 4
Problem Set 5
Problem Set 6
Resources
Administrivia
Standard Normal (Phi)
Practice Midterm
Practice Midterm Soln
Midterm Review Session
Notation Reference
Video Examples
Titanic Dataset
Machine Learning Datasets
Practice Final
Practice Final Soln
Extra Practice Problems
Demos
Serendipity
Medical Tests
Representative Juries
Normal Calculator
CS109 Logo
Beta
Likelihood
Office Hours
Overview
Video Examples
Created by Chris Piech. You know I love you cus I make videos for you on the weekend :).
Midterm Redux
Maximum Likelihood Lecture Problems