Summary

This course is an introduction to artificial intelligence. We will cover the most fundamental yet important algorithms and methods in this field and illustrate application areas such as natural language processing, speech recognition, computer vision and robotics.

Topics include:

• Problem solving using search
• Game playing and game theory
• Probabilistic reasoning under uncertainty
• Markov decision processes and reinforcement learning
• Machine learning using decision trees, neural nets et al.

Lectures

Time: Tuesday/Thursday 4:10 pm -5:25 pm; Location: Medical Sciences 129

Instructor

Shaojun Wang
387, Joshi Center
shaojun.wang(at)wright.edu
(937) 775-5140
Office hours: Tuesday/Thursday 5:30PM-7:00PM

Textbook

Russell, S. and Norvig, P.
Artificial Intelligence: A Modern Approach
Second edition
Prentice Hall, 2003

Course Grades and Workload

Three Homeworks 60%
Midterm exam (in class) 15%
Final exam (take home) 15%
Attendance 10%

Prerequisites

Data Structure
Linear Algebra
Probability and Statistics
Programming language: C++, Java, matlab