The class starts by providing a fundamental grounding in combinatorics, and then quickly moves into the basics of probability theory. We will then cover many essential concepts in probability theory, including particular probability distributions, properties of probabilities, and mathematical tools for analyzing probabilities. Finally, the last third of the class will focus on data analysis and Machine Learning as a means for seeing direct applications of probability in this exciting and quickly growing subfield of computer science.

Note on slides: In class I sometimes use slides to facilitate lecture. On their own they are not a great study resource. Here is a link to the slides directory in case you have use for them.

The schedule is subject to change by the management at any time.

Week Monday Wednesday Friday
1

Read: Ch. 1.1 - 1.2

Sept 24th

1: Counting

Read: Ch. 1.3 - 1.6

Sept 26th

2: Permutations and Combinations

Read: Ch. 2.1 - 2.5, 2.7

Sept 28th

3: Axioms of Probability
2

Read: Ch. 3.1 - 3.3

Oct 1st

4: Conditional and Bayes

Read: Lecture handout

Oct 3rd

5: Independence
Due: PSet #1
Read: Ch 3.4 - 3.5

Oct 5th

6: E[X] of Random Vars
3

Read: Chapter 4.1-4.4

Oct 8th

7: Var(X) of Random Vars

Read: Chapter 4.5-4.6

Oct 10th

8: Poisson Distribution

Read: Chapter 4.7-4.10

Oct 12th

9: Continuous Distributions
4
Due: PSet #2
Read: Chapter 5.1-5.3

Oct 15th

10: Normal Distribution

Read: Chapter 5.4-5.6

Oct 17th

11: Multivariable Models

Read: Chapter 6.1

Oct 19th

12: Continuous Multivariable
5

Read: Chapter 6.2-6.3

Oct 22nd

13: Conditional Distributions
Due: PSet #3
Read: Chapter 6.4-6.5

Oct 24th

14: Multivariate Properties

Read: Chapter 7.1-7.2

Oct 26th

15: Cov and Corr
6
Midterm: Tuesday, Oct 30th
Read: Chapter 7.3-7.4

Oct 29th

16: Great Expectations

Read: Chapter 7.5-7.6

Oct 31st

17: Beta

Read: Chapter 7.7

Nov 2nd

18: Central Limit Theorem
7

Read: Chapter 8.1-8.2, 8.5

Nov 5th

19: Sampling
Due: PSet #4
Read: Chapter 8.3-8.4

Nov 7th

20: General Inference

Read: Lecture Handout

Nov 9th

21: Parameters and MLE
8

Read: Lecture Handout

Nov 12th

22: Gradient Ascent

Read: Lecture Handout

Nov 14th

23: Maximum A Posteriori
Due: PSet #5
Smoke Day

Nov 16th

24: Naive Bayes
9

Nov 19th

Thanksgiving
No class

Nov 21st

Thanksgiving
No class

Nov 23rd

Thanksgiving
No class
10

Read: Lecture handout

Nov 26th

24: Naive Bayes

Read: Lecture handout

Nov 28th

25: Logistic Regression

Read: Lecture handout

Nov 30th

26: Deep Learning
11

Thanks Julia!

Dec 3rd

27: CS109 Overview

Our last class

Dec 5th

28: Beyond CS109
Due: PSet #6
Final: Dec 12th 3:30-6:30pm

Dec 7th

Dead Day
No class