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: Monday/Wednesday 4:10 pm - 5:25 pm; Location: Russ 158

Instructor

Shaojun Wang
387, Joshi Center
shaojun.wang(at)wright.edu
(937) 775-5140
Office hours: Monday/Wednessday 5:30 pm - 6:30 pm or by appointment

Textbook

Russell, S. and Norvig, P.
Artificial Intelligence: A Modern Approach
Third edition
Prentice Hall, 2010

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 such as the material covered in First Course in Probability by Sheldon Ross
Programming language: C++, Java, matlab