S11_CSE307_Introduction_to_Artificial_Intelligence

=CSE307: Introduction to Artificial Intelligence=

Program:
BS(CS)

Semester:
Spring 2011

Instructor:
Syeda Saleha Raza

**Course Lead:**
Dr. Sajjad Haider

**Credit Hours:**
3

Prerequisite(s):
CSE246, MTS201

Course Description:
This course provides an overview of the theoretical and practical aspects of designing intelligent computer systems. Students are expected to implement the concepts learned during the course using standard and AI-specific programming languages and tools. Topics included are history and overview of artificial intelligence, state space representation, uninformed and informed search techniques, search in games, decision trees, neural networks, evolutionary algorithms, propositional and predicate logic, inference in logic, probabilistic reasoning, robotics and various machine learning and computational intelligence techniques. .

Course Objectives:

 * To provide a broad survey of AI
 * To convey enthusiams of the field
 * To develop a deeper understanding of the core competences of AI
 * To develop the modeling/programming skills that will help you in building intelligent systems

URLs (Optional):
http://cse307ai-s2011.wikispaces.com .

Books:
Tim Jones, // Artificial Intelligence: A Systems Approach //, 2007. Ben Coppin, // Artificial Intelligence Illuminated //, 2004. Steven Rabin, // AI Game Programming Wisdom 3 //, 2005. Steve Rabin, // AI Game Programming Wisdom 4 //, 2008

Web Resources:
Any web resources that may be used during the course.

Grading Policy:
Assignment: 7.5% Quiz: 7.5% Project: 15% Midterm I: 15% Midterm II: 15% Final: 40%

Uploaded Reference Course Outline:
You may also upload a course outline here.

Class Time Spent On (In Credit Hours):
Theory: 1.8 Problem Analysis: 0.5 Solution Design: 0.5 Social and Ethical Issues: 0.2

Topics Covered in the Course

 * Week || Topic of Lecture ||
 * 1 || History and Introduction of AI, Search Space, Problem Solving as Search ||
 * 2 || Depth-first Search, Breadth-first Search, Best-first Search, A*, Greedy Algorithms, Hill Climbing ||
 * 3 || Search in Adversarial Games, Min-Max Search, Alpha-Beta Pruning ||
 * 4 || Introduction to Machine Learning, Classification Trees (Gini Index, Entropy) ||
 * 5 || Classification Trees (Cont'd), Naive Bayes ||
 * 6 || Naive Bayes (Cont'd), Neural Networks ||
 * 7 || Neural Networks (Cont'd), Backpropagation Algorithm ||
 * 8 || Review of Midterm I, Overview of Clustering Techniques, K-Means ||
 * 9 || K-Means (Cont'd), Propositional and Predicate Logic ||
 * 10 || Logic (Cont'd), Prolog, Introduction to Bayesian Networks ||
 * 11 || Inference in Bayesian Networks ||
 * 12 || d-Separation, Markov Blanket, Serial, Converging and Diverging Connections, Tools ||
 * 13 || Evolutionary Algorithms ||
 * 14 || Computer Vision ||
 * 15 || Swarm Intelligence ||