# 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 znu.ac.ir

**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.

## Textbook

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

**Introduction to
AI**:
history (PDF), (AI Brain)
**Uncertainty in
AI**:
Non-monotonic Reasoning, Certainty Factor, Probabilistic Models (PDF), The Mechanization of Causal
Inference: A "Mini Turing Test" and Beyond (Video)
**Introduction to
Probability theory**: Basics (PDF)
**Probabilistic
Graphical Models**:
Causal and Bayesian Networks (PDF)
**Building Models:
**Variables,
Arcs and Dependencies** **(PDF)
**Inference in
Bayesian Network 1:** Variable Elimination Algorithm(PDF, Courtesy of Eyal Amir)
**Inference in
Bayesian Network 2:** Approximate Inference (PDF)
**Markov
Processes: **Markov Models,** **Hidden Markov Models(PDF)
**Markov Models:
Applications**:
(PDF, Courtesy of Jan Rupnic
)(Markov Chain Interpretation of Google Page Rank, PDF ,PDF)
**Hidden Markov
Models**:
(Particle Filtering, PDF)
**Markov Decision
Process: **(PDF) Reading Material (Scalable MDP Based Planning...)
**Reinforcement
Learning:**
(PDF)

## Assignments

Homework 1 (PDF) (RiskFactorData.csv)

Homework 2 (PDF)

Homework 3 (PDF)