Advanced Artificial Intelligence

No other field in computer science has higher visibility (and expectation, and disappointment) than artificial intelligence to the general population.  This course covers some advanced components of artificial intelligence as we know it, namely deductive reasoning agents, reactive agents,  probabilistic reasoning and Bayesian networks.   You should be able to build an example of a probabilistic model at the end of the semester.

Administrative Details

Here you will find administrative information for the Fall 1398.

Instructor: Dr. Mohsen Afsharchi, afsharchi at

Lectures: 3-5 Sat and 8-10 Wed

Prerequisites: Clear understanding of probability, common data structures, algorithms, standard programming and preferably some preliminary AI concepts. 


Some useful textbooks for this course are:

1.     Bayesian Networks and Decision Graphs, (Second Edition), by Finn V. Jensen and Thomas D. Nielsen, Springer 2007

2.     Artificial Intelligence: A Modern Approach (Third Edition), by Stuart J. Russell, Peter Norvig, Prentice Hall 2010 .

3.     An Introduction to Multiagent Systems , (Second Edition), by Michael Wooldridge, Wiley 2009


Lecture material will be drawn from textbooks, as well as from some of the recent online materials.

Course Syllabus and Lecture Notes




Homework 1 (PDF) (RiskFactorData.csv)
Homework 2 (PDF)

Homework 3 (PDF)