Course Outline  
Lecture (CSI 2118):  TuTh 12:30pm  1:45pm. P. S. Krishnaprasad 
Discussion (EGR 2112):  M 9:00am9:50am. Biniyam Tesfaye Taddese, TA 
PSK Office Hours (AVW 2233):  M 4:006:00 pm and Tu 5:007:00 pm. 
TA Office Hours (EGL 1205):  We 10:30am 12:30pm 
Special Announcements(5) Typos in lecture 4 fixed. Lectures 5 and 6 posted.(4) Applications of Bayes Theorem notes posted. There are still typos to be fixed on page 9. If you find others, please let me know. (3) Read example on page 4 of lecture notes on conditioning, then go back to page 1 of the notes. Examine the robot merchant problem closely. (2) Read ahead on conditional probability (pages 815). (1) Read pages 18 of Chapter 1 of Grimmett and Stirzaker. Discussion on Monday January 29 will include this material. 
Lecture Notes by P. S. KrishnaprasadLecture 1 (Basic Concepts)
Lecture 2 (Counting)
Lecture 3 (Conditioning)
Lecture 4 (Applications of Bayes' Theorem)
Lecture 5 (Random Variables)
Lecture 6 (Inequalities)

Homework AssignmentsGroup effort in working out homework problems is acceptable. However everyone should submit individual homework solutions. Precise credit for any sources used (colleagues, teachers, journal articles, books, web resources etc.) should be given.Running list of exercises. 
Some interesting resources on the web
Wiki on Monty Hall ProblemWiki on Bayesian ProbabilityStanford Encyclopedia of Philosophy on Bayes' TheoremCornell site on Bayesian Inference in the Physical Sciences 