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.
The schedule is subject to change by the management at any time.
Week  Monday  Wednesday  Friday 

1  
2 
Chapter 3.13.3 Oct 2nd Conditional Probability, Bayes TheoremSlides Lecture Notes Medical Test Demo 

3  
4 
Read: Chapter 5.45.6 Oct 18th Joint Distributions 
Read: Chapter 6.1 Oct 20th Continuous Joint Distributions 

5 
Read: Chapter 6.26.3 Oct 23rd Properties of Joint Distributions 
Due: PSet #3 Read: Chapter 6.46.5 Oct 25th Convolution and Joint Conditional 
Read: Chapter 7.17.2 Oct 27th Beta Distributions 
6 
Read: Chapter 7.37.4 Oct 30th Great Expectations 
Midterm: Thursday, Nov 2nd Read: Chapter 7.57.6 Nov 1st Covariance and Correlation 
Read: Chapter 7.7 Nov 3rd Samples 
7 
Read: Chapter 8.18.2, 8.5 Nov 6th Central Theorems 
Due: PSet #4 Read: Chapter 8.38.4 Nov 8th Parameters and MLE 
Read: Lecture Handout Nov 10th Maximum A Posteriori 
8 
Read: Lecture Handout Nov 13th Naive Bayes 
Read: Lecture Handout Nov 15th Logistic Regression 
Due: PSet #5 Read: Lecture Handout Nov 17th Intro to Deep Learning 
9 
Nov 20th Thanksgiving 
Nov 22nd Thanksgiving 
Nov 24th Thanksgiving 
10 
Nov 27th CS109 Overview 
Nov 29th Deep Learning II 
Dec 1st Probabilistic Graphical Models 
11 
Dec 4th CS109 Overview 
Dec 6th Final Review 
Due: PSet #6 Dec 8tb Dead Day 