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.
While we do not have required reading for this class, the suggested readings come from the optional textbook S. Ross, A First Course in Probability (9th Ed.), Pearson Prentice Hall, 2013. Copies of the book are available for purchase at the Stanford Bookstore, and are also available on reserve in the Engineering Library.
The schedule is subject to change by the management at any time.
Week  Monday  Wednesday  Friday 

1 
Read: Ch. 1.3  1.6 June 27th Permutations and Combinations 

2 
Read: Chapter 3.13.3 July 2nd Conditional Probability, Bayes TheoremSlides Lecture Notes Medical Test Demo 
July 4th Independence Day 

3  
4  
5 
Midterm: Tue 7:009:00p Read: Chapter 6.46.5 July 23rd Independent Random Variables 
Read: Chapter 6.4, 5.6.1, 5.6.4 July 25th Conditional Distributions,Beta Distribution 
Read: Chapter 7.37.4 July 27th Correlation and Covariance 
6 
Due: PSet #4 Read: Chapter 7.17.2 July 30th Advanced Expectation 
Read: Lecture Notes Aug 1st Samples/Bootstrapping 
Read: Chapter 8.18.2, 8.48.5 Aug 3rd Probability bounds 
7 
Due: PSet #5 Read: Chapter 8.3 Aug 6th Central Limit Theorem 
Read: Lecture Notes Aug 8th Maximum Likelihood,Maximum A Posteriori 
Read: Lecture Notes Aug 10th Naive Bayes 
8 
Due: PSet #6 Lecture Notes Aug 13th Logistic Regression 
Aug 15th Read: Lecture Notes 
Aug 17th Final exam: 3:306:30pm 