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CS109
Lecture Notes
1 - Counting
2 - Combinatorics
3 - Probability
4 - Cond Probability
5 - Independence
6 - Random Variables
7 - Variance
8 - Poisson
9 - Continuous
10 - Gaussian
11 - Joint
12 - Continuous Joint
13 - Joint Properties
14 - Tracking
15 - Convolution
16 - Beta
17 - Great Expectations
18 - Correlation
19 - Samples and Bootstrap
20 - Central Limit Theorem
21 - Maximum Likelihood
22 - Gradient Ascent
23 - Maximum A Posteriori
24 - NaiveBayes
25 - LogisticRegression
26 - DeepLearning
Problem Sets
Problem Set 1
Problem Set 2
Problem Set 3
Problem Set 4
Problem Set 5
Problem Set 6
PSet 1 Soln
PSet 2 Soln
PSet 3 Soln
PSet 4 Soln
PSet 5 Soln
PSet 6 Soln
Sections
Section 1
Section 2
Section 3
Section 4
Section 6
Section 7
Section 8
Section 1 Soln
Section 2 Soln
Section 3 Soln
Section 4 Soln
Section 6 Soln
Section 7 Soln
Section 8 Soln
Handouts
Course Reader
Administrivia
Calculation Ref.
Notation
Standard Normal Phi
Python for Probability
Practice Midterm
Practice Midterm Soln
Extra Midterm Practice Problems
Midterm Solutions
Contest
Practice Final
Practice Final Solution
Serendipity
Medical Tests
Galton Board
Jurors
Normal CDF
Beta
Central Limit
Bootstrap
Likelihood
Office Hours
Schedule
CS109 Midterm
Midterm
Midterm
Midterm Soln
Midterm PDF
Midterm CDF
Midterm Correlation Matrix
Midterm Correlation Matrix
