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CS109
Lectures
Week 1
1 - Counting
Slides
Lecture Notes
2 - Permutations and Combinations
Slides
Lecture Notes
3 - Axioms of Probability
Slides
Lecture Notes
Week 2
4 - Conditional Probability and Bayes
Slides
Lecture Notes
5 - Independence
Slides
Lecture Notes
6 - Random Variables and Expectation
Slides
Lecture Notes
Week 3
7 - Variance, Bernoulli, Binomial
Slides
Lecture Notes
8 - Poisson and More
Slides
Lecture Notes
9 - Continuous Random Variables
Slides
Lecture Notes
Week 4
10 - The Normal Distribution
Slides
Lecture Notes
11 - Joint Distributions
Slides
Lecture Notes
12 - Continuous Joint Distributions
Slides
Lecture Notes
Week 5
13 - Independent Random Variables
Slides
Lecture Notes
14 - Conditional Distributions
Slides
Lecture Notes
15 - Correlation and Covariance
Slides
Lecture Notes
Week 6
16 - Great Expectations
Slides
Lecture Notes
17 - Beta
Slides
Lecture Notes
18 - Central Limit Theorem
Slides
Lecture Notes
Week 7
19 - Sampling/Bootstrapping
Slides
Lecture Notes
20 - General Inference
Slides
Lecture Notes
21 - Parameters and MLE
Slides
Lecture Notes
Week 8
22 - Gradient Ascent
Slides
Lecture Notes
23 - Maximum A Posteriori
Slides
Lecture Notes
24 - MAP + Naive Bayes
Slides
Lecture Notes
Week 9
25 - Naive Bayes + Logistic Regression
Slides
Lecture Notes
26 - Logistic Regression + Deep Learning
Slides
Lecture Notes
27 - Deep Learning II
Slides
Problem Sets
Problem Set 1
Problem Set 2
Problem Set 3
Problem Set 4
Problem Set 5
Problem Set 6
PS 1 Solution
PS 1 Problem 16 solution
PS 2 Solution
PS 3 Solution
PS 4 Solution
PS 5 Solution
PS 6 Solution
Midterm
Midterm Solution
Contest
Final Solution
Section
Section 1
Handout
Solutions
Section 2
Handout
Solutions
Section 3
Handout
Solutions
Section 4
Handout
Solutions
Section 5
Handout
Solutions
Section 6
Handout
Jupyter Notebook
Solutions
Section 7
Handout
Jupyter Notebook
Solutions
Section 8
Handout
Solutions
Section 9
Jupyter Notebook
Notebook Solutions
Section Attendance
Concept Checks
Concept Check Answers
Handouts/Demos
Administrivia
Calculation Ref
Python for Probability
Python Session Slides
Standard Normal Table
Normal CDF Calculator
Midterm Info/Practice
Final Info/Practice
Serendipity
Medical Bayes
Galton Board (Binomial)
Jurors
Federalist Papers
Dart (Joint)
Beta
Bootstrap
WebMD (Inference)
MLE and Gradient Ascent
Staff / Office Hours
Schedule
CS109 Teaching Team (and Office Hours)
Location:
Where are office hours?
Teaching Team
Instructor: Lisa Yan
yanlisa @ stanford
Gates 187
W 3:00-5:00pm
Head TA: Noah Arthurs
narthurs @ stanford
Th 9:30am Thornton 211
Th 10:30am Thornton 211
TA: Oishi Banerjee
oishib @ stanford
W 6:30pm 160-319
W 7:30pm 160-319
TA: Tim Gianitsos
tgianit @ stanford
F 3:30pm 160-323
F 4:30pm 160-323
TA: Sonja Johnson-Yu
sonjyu @ stanford
W 3:30pm Lathrop 290
W 4:30pm Lathrop 290
TA: Cooper Raterink
crat @ stanford
W 5:30pm Lathrop 290
W 6:30pm Lathrop 290
TA: Gili Rusak
gili @ stanford
Th 4:30pm 200-201
Th 5:30pm 200-201
TA: Alex Tseng
amtseng @ stanford
F 9:30am 160-317
F 10:30am 160-317
TA: Alex Tsun
alextsun @ stanford
Th 1:30pm Lathrop 290
Th 2:30pm Lathrop 290
TA: Julie Wang
juliesw @ stanford
Th 6:30pm 200-201
Th 7:30pm 200-201
Winter CS109 Instructor : David Varodayan
varodayan @ stanford
Gates 161
Th 10:00am-12:00pm