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 SPRING BREAK 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