CS430: Introduction to Artificial Intelligence
Course Description
The goal of Artificial Intelligence is to build software systems that
behave "intelligently". By this, we mean that the computer systems
"do the right thing" in complex environments--that they act optimally
given the limited information and computational resources available.
This course provides an introduction to artificial intelligence. We
will first study the core topics of knowledge representation,
reasoning, and learning, all from the perspective of probabilistic
methods. Then we will cover several of the "subject areas" of
artificial intelligence where these probabilistic methods are applied
including Natural Language Processing, Perception (primarily vision),
and Robotics.
Prerequisites: CS325; CS381; experience programming in Java
Registration Information: 4 Units. MWF 12:00 Nash 206. CRN 12475
Instructor: Thomas
G. Dietterich
Teaching Assistant: Hongli Deng
Course Handouts
Viewgraphs for Lectures
- Part 1 (Introduction, Chapter 1)
Powerpoint
- Part 2 (Rational Agents, Chapter 2)
Powerpoint
- Part 3 (Search, Chapters 3-4)
Powerpoint
- Part 4 (Reasoning, bits of
Chapters 7, 8, and 9)
- Part 5 (Probability and Bayesian
Networks, Chapters 13-14)
- Part 6 (Statistical Learning and
Spam Filtering)
- Part 7 (Probabilistic Reasoning
over Time, Chapter 15)
- Part 8 (Statistical Learning: The
complex cases, Chapter 20.3)
- Statistical Natural Language Processing
(Prof. Tadepalli's guest lecture; Chapter 23.1, 23.2)
- Part 9 (Natural Language
Processing, Chapter 22, 23.3, 23.4)
- Part 10 (Computer Vision, Chapter
24)
- Part 11 (Robotics, Chapter 25)
- Part 12 (Philosophical
Foundations, Chapter 26)
Homework Assignments
Note: Solutions are available to registered students through the
Blackboard system.
Programming Assignments
Quarter Project
Other Stuff