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CS109
Lectures
Week 1
1 - Counting
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2 - Permutations and Combinations
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3 - Axioms of Probability
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Week 2
4 - Conditional Probability and Bayes
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5 - Independence
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6 - Random Variables and Expectation
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Week 3
7 - Variance, Bernoulli, Binomial
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8 - Poisson and More
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9 - Continuous Random Variables
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Week 4
10 - The Normal Distribution
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11 - Joint Distributions
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12 - Independent Random Variables
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Week 5
13 - Joint Random Variable Statistics
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14 - Conditional Expectation
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15 - General Inference
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Week 6
16 - Continuous Joint Distributions I
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17 - Continuous Joint Distributions II
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18 - Central Limit Theorem
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Week 7
19 - Sampling/Bootstrapping
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20 - Parameters and MLE
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21 - Beta
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Week 8
22 - Maximum A Posteriori
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23 - Naive Bayes
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Week 9
24 - Gradient Ascent and Linear Regression
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25 - Logistic Regression
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26 - Utility of Money, Simulating Probabilities, and Jensen's Inequality
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Week 10
27 - Deep Learning & Beyond CS109
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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 Q14 explanation
PS 2 Solution
PS 3 Solution
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PS 5 Solution
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Section
Section 1
Warmups
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Section 2
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Section 3
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Section 4
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Section 5
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Section 6
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Section 7
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Section 8
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Section Attendance
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Administrivia (Updated May 13)
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Python for Probability
Quick Guide to LaTeX
Python Session Slides
Standard Normal Table
Normal CDF Calculator
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Probability Reference
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Serendipity
Medical Bayes
Galton Board (Binomial)
Federalist Papers
Beta
Bootstrap
WebMD (Inference)
Central Limit Theorem
MLE Uniform
Gradient Ascent
Simulating Probabilities
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Schedule
Problem Set 2: Core Probability
Due: Monday, April 27, 10:00AM Pacific Time
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