CMPSCI 291b: Spring 2008

Syllabus and Course Schedule

Prof. David Mix Barrington

Reading assignments are from Barrington, A Mathematical Foundation for Computer Science, fourth draft. Copies of the relevant sections will be made available to students.

Lecture class meets MWF 9:05-9:55 and discussion class meets Fridays 10:10-11:00. All class meetings are in room 140 of the computer science building.

This is a course under construction! The syllabus may change as the term progresses.

PART I:  Basic Probability and Counting

Mon 28 Jan L01  Course Overview 
Wed 30 Jan L02  Basic Probability Definitions (P.1)
Fri 01 Feb L03  The Four Counting Problems
Fri 01 Feb D01  Starting the Sequence-Lister (6.8)
Mon 04 Feb L04  Sum and Product Rules (6.1)
Wed 06 Feb L05  Double-Counting and Inclusion/Exclusion (6.2)
Fri 08 Feb L06  First and Second Counting Problems (6.3,6.4)
Fri 08 Feb D02  More on the Sequence-Lister
Mon 11 Feb L07  Third Counting Problem (6.6)
Wed 13 Feb L08  Counting Poker Hands (6.6)
Fri 15 Feb L09  Fourth Counting Problem (6.7)
Fri 15 Feb L09  Finishing the Sequence-Lister

PART II: Probability and Expected Value

Tue 19 Feb L10  Sum and Product Rules for Probability (P.1)
Wed 20 Feb L11  Expected Value (P.2)
Fri 22 Feb L12  Evaluating Games (P.2)
Fri 22 Feb D04  Representing Poker Hands
Mon 25 Feb L13  Variance and Standard Deviation (P.3)
Wed 27 Feb L14  Examples of Distributions (P.3)
Fri 29 Feb L15  Binomial Distributions (P.4)
Fri 29 Feb D05  Modelling Texas Hold'em
Mon 03 Mar L16  The Coupon Collector's Problem (P.5)
Wed 05 Mar L17  Bounds on Probability (P.6,P.7 skim)
Fri 07 Mar L18  Monte Carlo Simulation
Fri 07 Mar D06  Different Approaches to Texas Hold'em
Mon 10 Mar L19  Lessons from the Texas Hold'em Project
Wed 12 Mar L20  Review for Midterm
Fri 14 Mar X01  MIDTERM EXAM using both lecture and discussion period


PART III: Bayesian Reasoning

Mon 24 Mar L21  Conditional Probabilities (P.8)
Wed 26 Mar L22  Bayes' Theorem (P.8)
Fri 28 Mar L23  Examples of Bayesian Reasoning
Fri 28 Mar D07  Getting Word-Instance Vectors
Mon 31 Mar L24  The Naive Bayes Classifier
Wed 02 Apr L25  More on the NBC
Fri 04 Apr L26  Introduction to Bayes Nets
Fri 04 Apr D08  Starting the Classifier
Mon 07 Apr L27  Problems with the NBC
Wed 09 Apr L28  Other Approaches to Stochastic Learning
Fri 11 Apr L29  Summary of Stochastic Learning
Fri 11 Apr D09  Finishing the Classifier

PART IV: Markov Processes

Mon 14 Apr L30  State Machines and the Markov Rule
Wed 16 Apr L31  Markov Processes
Fri 18 Apr L32  Modelling the Backgammon Endgame
Fri 18 Apr D10  Matrices and Graphs
Wed 23 Apr L33  Long-Term Behavior of Markov Processes
Fri 25 Apr L34  Markov Decision Processes
Fri 25 Apr D11  Starting the Backgammon Simulator
Mon 28 Apr L35  Discounting and Horizons
Wed 30 Apr L36  Finding Optimal Policies
Fri 02 May L37  Monte Carlo Markov Chains
Fri 02 May D12  Finishing the Backgammon Simulator
Mon 05 May L38  Game Theory -- The Prisoner's Dilemma
Wed 07 May L39  Classical Game Theory
Fri 09 May L40  Solving Zero-Sum Games
Fri 09 May D13  Course Evaluations
Mon 12 May L41  Course Review

Final Exam to be arranged by the University

Last modified 27 January 2008