Artificial Intelligence II

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 probabilistic reasoning, Bayesian networks and machine learning.   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 1387.

.       Instructor: Dr. Mohsen Afsharchi, afsharchim at znu.ac.ir

.       Lectures: 9:30-11 Wed and Thr

.       Office Hours:  Wednesday and Thursday

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

Textbooks

The required textbooks for this course are:

.      

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

 
 
  

.      

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, by Judea Pearl , Morgan Kauffman 1988 

 
  

A supplementary textbook (recommended, but not required) is:

.       Bayesian Artificial Intelligence, by Kevin B. Korb, Ann E. Nicholson , Chapman & Hall, 2003 

.       Artificial Intelligence: A Modern Approach (Second Edition), by Stuart J. Russell, Peter Norvig, Prentice Hall 2002 .

.       Artificial Intelligence: A New Synthesis, by Nils J. Nilsson. Morgan Kaufmann 1999.

 

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

Course Syllabus and Lecture Notes

  1. Introduction AI: history (PDF)
  2. Uncertainty in AI: Non-monotonic Reasoning, Certainty Factor, Probabilistic Models (PDF)
  3. Introduction to Probability theory: Basics(PDF)
  4. Probabilistic Graphical Models: Causal and Bayesian Networks(PDF)
  5. Assignment 1
  6. Inference in Bayesian Network 1: Exact Inference in Singly Connected Networks(PDF)
  7. Inference in Bayesian Network 2: Exact Inference in Chains and Trees(PDF), Variable Elimination Algorithm(PDF, Courtesy of Eyal Amir)
  8. Building Models: Variables, Arcs and dependencies (PDF)

 

 

 

 

 

 

Course Work and Evaluation

  

Possibly Interesting URLs

Here is an ad hoc collection of relevant AI links and interesting tidbits. If you know of other good stuff to share with your classmates here, please let me know, and I will try to add it.