CSE 327    Artificial Intelligence Theory and Practice (3)

Instructor:   Jeffrey Heflin
 
Current Catalog Description
Introduction to the field of artificial intelligence: Problem solving, knowledge representation, reasoning, planning and machine learning. Use of AI systems or languages. Advanced topics such as natural language processing, vision, robotics, and uncertainty. Prerequisite: CSE 12 or 15

Textbook
Russell, Stuart and Norvig, Peter, Artificial Intelligence: A Modern Approach, 2nd Ed.. 2003, Prentice Hall.

References 

Course Goals  
1. Understanding of basic AI concepts
2. Knowledge of some examples of state of the art
3. Overview of the subfields of AI

Prerequisites by Topic
1. Comprehending a pseudo-code algorithm
2. Basic data structures (e.g., trees, graphs)

Major Topics Covered in the Course

1. Agents
2. Uniformed and informed search
3. Logic and knowledge representation
4. Planning
5. Uncertainty
6. Machine learning

Laboratory projects (specify number of weeks on each)
none

Estimate CSAB Category Content
                                                                      CORE        ADVANCED
Data Structures                                                                        1.0  
Computer Organization and Architecture 
Algorithms Software Design                                                      2.0  
Concepts of Programming Languages   

Oral and Written Communications
Every student is required to submit at least  __0__  written reports (not including exams, tests, quizzes, or commented programs) of typically  __0__  pages and to make  __0__  oral presentations of typically  __0__  minutes duration. Include only material that is graded for grammar, spelling, style, and so forth, as well as for technical content, completeness, and accuracy.

Social and Ethical Issues
Parts of the first and last lecture discuss the ethical obligations of building intelligent machines. Students are not graded on this material.

Theoretical Content
The majority of course focuses on understanding various algorithms for solving AI problems, such as search, logic inference, planning, and machine learning. However, complexity of the algorithms is only discussed informally, due to the varied backgrounds of many of the students. About two weeks of the course look at various representations for general-purpose knowledge, including propositional logic, first order logic, and semantic networks.

Problem Analysis
There are seven homework assignments that force the students to think about the way the algorithms work. Some of these ask students to compare different ways for solving the same problem, or to walk through an algorithm and show how it derives an answer.

Solution Design
One assignment typically involves writing one or two small Prolog programs.