Fall , 2015







Lecture 
Date 
Lecture & Lab Topics 
Readings, from DeGroot unless otherwise noted 
Homeworks and Tests 
Labs 
1  R 9/3  Administrative matters; Goals of the course; Motivating Examples: Why should a computer scientist know probability and statistics?  Recommended: Chapter One from "The Drunkard's Walk": PDF Here is a link to an article on traffic deaths after 9/11, with slightly different numbers than I presented: HTML Here is the wiki page explaining the "Base Rate Falacy" (the breathalyzer example at the end of class): HTML Just for Fun: Here is a short YT video with various clips from movies which involve probability (we will return to the Monty Hall Problem, the first clip, next week): HTML 

2  T 9/8  Classical Probability: Basic definitions; Set theory (e.g., Venn Diagrams) as a model for Sample Spaces and Events; Geometric Probability (uncountable sample spaces).  Sections 1.1  1.4  
3  R 9/10  Axioms and theorems of probability; Geometric Probability (uncountable sample spaces);  Section 1.5  We will cover this in detail; pay particular attention to last two pages, on uncountable sample spaces! Section 1.6  We basically covered this in class today, but review it! 
HW 01: HTML Solution: HTML 

M 9/14  Lab 01: Generating random numbers and running simulations  Lab 01: HTML Solution: TXT 

4  T 9/15  The general Inclusion/Exclusion Principle; Combinatorics and counting finite sets; Multiplication principle and tree diagrams; The Monty Hall Problem (application of the Multiplication Principle); Choosing with and without replacement; Permutations. 
Read this short article on the InclusionExclusion Principle: HTML and then read page 48 (skipping the proof if you like....) in the textbook. Sections 1.7 (main reading) Also Google "probability tree diagram" and read the first link (easy) and then read sections 14.1 and 14.2 of the following analysis of the "Monty Hall Problem": PDF 

5  R 9/17  Counting principles continued; permutations and combinations; accounting for duplicates; applications to classical probability. 
Section 1.8  HW 02: HTML Solution: HTML 

M 9/21  Lab 02: Computing with permutations and combinations; simulation continued.  Read about Pascal's Triangle and its relationship to C(N,K) here.  Lab 02: HTML Solution: TXT 

6  T 9/22  Counting concluded: combinations and subsets; accounting for repetitions; multinomial coefficients;  Section 1.8, Read this link on Permutations with Repetitions; Section 1.9 

7  R 9/24  Discrete nonequiprobably sample spaces; Histograms vs distributions; Conditional Probability  Read about Histograms here: Section 2.1 
HW03: HTML Solution: HTML 

M 9/28  Lab 03: Drawing probabilities in Python: PMFs and CDFs  Lab 03: HTML Solution: TXT 

8  T 9/29  Conditional Probability; Independence  Section 2.2  Quiz 01 at end of class;  
9  R 10/1  Bayes Theorem, Discrete Random Variables; Distributions, PMFs and CDFs  Read about Bayes Theorem here: HTML Just read the first two screenfuls or so, with the background, statement of the theorem and the example about Addison. Read chapter 3.1 up to page 98 on Discrete Random Variables. This is just a way of formalizing what we have already been doing! 
HW04: HTML Solution: HTML 

M 10/5  Lab 04  Lab 04: HTML Solution: TXT 

10  T 10/6  Properties of random variables: Expected value/mean, mode, variance, standard deviation, skew  Our textbook spreads out the various characteristics of random variables, and I would rather you read Chapter Five from the Schaum's, which I provide since not all of you will have bought it: PDF This reading uses f(x) instead of p(x) for the PMF, but otherwise it is fairly consistent with what we have been doing. 

11  R 10/8  Bernoulli Trials, Binomial Distribution  HW05: HTML Solution: HTML 

T 10/13  Monday Schedule; Lab 05  
12  R 10/15  Poisson Distribution  HW05: HTML Solution: HTML 

M 10/19  Lab 06  Lab 05: HTML Solution: TXT 

13  T 10/20  Geometric Distribution;  
14  R 10/22  Joint random variables & joint distributions  HW06: HTML Solution: HTML 

M 10/26  Lab 07  Lab 06: HTML Solution: TXT 

15  T 10/27  Covariance, Correlation  HW07 (short): HTML Solution: HTML 

W 10/28  Midterm Review Session at 7pm, location TBA 


R 10/29  Midterm (tentative) 


M 11/2  Lab 08  Lab 07: HTML Solution: TXT 

16  T 11/3  Regression and curve fitting  
17  R 11/5  Continuous random variables; the normal distribution;  HW08: HTML Solution: HTML 

M 11/9  Lab 09: Approximating the binomial with the normal distribution; Continuity correction for normal approximation to binomial  Lab 08: HTML Solution: TXT 

18  T 11/10  Central Limit Theorem; Law of Large Numbers  
19  R 11/12  Continuous Distributions: Poisson  HW09: HTML Solution: HTML 

M 11/16  Lab 10: Demonstrating the CLT  Lab 09: HTML Solution: TXT 

20  T 11/17  Continuous Distributions: Exponential  
21  R 11/19  Intro to Statistical Thinking: Sampling and sample statistics  HW03: HTML Solution: HTML 

M 11/23  Lab 11:  Lab 10: HTML Solution: TXT 

22  T 11/24  Statistics:  
23  R 11/26  Statistics  HW03: HTML Solution: HTML 

M 11/30  Lab 12  Lab 11: HTML Solution: TXT 

24  T 12/1  Statistics  
25  R 12/3  Markov Processes  HW03: HTML Solution: HTML 

M 12/7  Lab 13  Lab 12: HTML Solution: TXT 

26  T 12/8  Markov Processes  
27  R 12/10  HW03: HTML Solution: HTML 

R 12/17  Final Exam 12:30  2:30pm 