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.
Lecture content is subject to change by the management at any time.
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#  Weekday  Date  Topic  Notes 

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Week 1  
3

1  Mon  Jan 3  Counting  
4

2  Wed  Jan 5  Combinatorics  PSet 1 out 
5

3  Fri  Jan 7  What is Probability?  
6

Week 2  
7

4  Mon  Jan 10 
Conditional Probability and Bayes


8

5  Wed  Jan 12  Independence  
9

6  Fri  Jan 14 
Random Variables and Expectation

PSet 1 in / PSet 2 out

10

Week 3  
11

Mon  Jan 17  No Class (MLK Day)  
12

7  Wed  Jan 19 
Variance Bernoulli Binomial


13

8  Fri  Jan 21  Poisson  
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Week 4  
15

9  Mon  Jan 24 
Continuous Random Variables

PSet 2 in / PSet 3 out

16

10  Wed  Jan 26  Normal Distribution  
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11  Fri  Jan 28  Joint Distributions  
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Week 5  
19

12  Mon  Jan 31  Continuous Joint  
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13  Wed  Feb 2  Inference  
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14  Fri  Feb 4  Modelling  PSet 3 in 
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Week 6  
23

Mon  Feb 7  No Class (Break)  
24

Tue  Feb 8  Midterm 
Midterm: 7  9pm


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15  Wed  Feb 9  General Inference  PSet 4 out 
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16  Fri  Feb 11  Beta  
27

Week 7  
28

17  Mon  Feb 14  Adding  
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18  Wed  Feb 16  Central Limit Theorem  
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19  Fri  Feb 18 
Bootstraping and PValues

PSet 4 in / PSet 5 out

31

Week 8  
32

Mon  Feb 21 
No Class (Presidents Day)


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20  Wed  Feb 23  Algorithmic Analysis  
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21  Fri  Feb 25  M.L.E. 
Withdraw deadline

35

Week 9  
36

22  Mon  Feb 28  M.A.P. 
PSet 5 in / PSet 6 out

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23  Wed  Mar 2  Naive Bayes  
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24  Fri  Mar 4  Logistic Regression  
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Week 10  
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25  Mon  Mar 7  Deep Learning  Challenge in 
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26  Wed  Mar 9  Future of Probability  PSet 6 in 
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Fri  Mar 11  No Class (Break) 
Final: Thurs, Mar 17th, 12:15  3:15pm

This quarter we are writing a Course Reader for CS109 which is free and written for the course. You can optionally read from Sheldon Ross, A First Course in Probability (10th Ed.), Prentice Hall, 2018. The corresponding readings can be found Win 21 schedule. The textbook's 8th and 9th editions have the same readings and section headers.