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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 - Conditional Joints
14 - Joint Properties
15 - Beta
16 - CLT
17 - Samples
18 - General Inference
19 - MLE
20 - Gradient Ascent
21 - MAP
22 - NaiveBayes
23 - LogisticRegression
24 - DeepLearning
EXTRA - Covariance
EXTRA - GreatExpectations
Problem Sets
Problem Set 1
Problem Set 2
Problem Set 3
Problem Set 4
Problem Set 5
Problem Set 6
PS1 Latex Template
PS2 Latex Template
PS3 Latex Template
PS4 Latex Template
PS5 Latex Template
PS6 Latex Template
PSet 1 Soln
PSet 2 Soln
PSet 3 Soln
PSet 4 Soln
PSet 5 Soln
Sections
Python Tutorial
Section 1
Section 2
Section 3
Polls Data
Section 4
Section 5
Section 6
Section 1 Soln
Section 2 Soln
Section 3 Soln
Section 4 Soln
Section 5 Soln
Section 6 Soln
Handouts
Course Reader
Administrivia
SCPD Info
Calculation Ref
Discrete Vars
Standard Normal Phi
Python for Probability
Midterm Info and Practice
Midterm Exam PDF
Midterm Solutions
Final Info and Practice
Final Solutions
Serendipity
Medical Tests
Notation
Galton Board
Jurors
Normal CDF
Dart Joint
Beta
Central Limit
Staff / Office Hours
Schedule
CS109 Teaching Team (and Office Hours)
Location:
Where are office hours?
Teaching Team
Instructor: Noah Arthurs
narthurs@stanford.edu
Thurs 12:00-2:00pm
Gates 202
TA: Oishi Banerjee
oishib@stanford.edu
TA: Ben Braun
bbraun@stanford.edu