Computer Science @ UC Davis | Course Descriptions

ECS 170 INTRODUCTION TO ARTIFICIAL INTELLIGENCE (4) II, III

Lecture: 3 hours

Discussion: 1 hour

Prerequisite: Course ECS 140A

Grading: Letter; homework (25%), project (25%), midterm (25%), final (25%)

Catalog Description:
Design and implementation of intelligent computer systems. Knowledge representation and organization. Memory and inference. Problem solving. Natural language processing.

Knowledge Representation
1. Representing alternatives
2. Descriptions
3. Natural constraints
Searching
1. Trees, graphs AND/OR trees
2. Breadth-first
3. Depth-first
4. Minimal cost
5. Minimax
6. Alpha-beta
Vision and Pattern Recognition
1. Feature extraction
Knowledge Representation
1. Frames
2. State space
Expert Systems
Theorem Proving
1. Semantic nets
2. Resolution
Planning
1. Synthesis
2. Plans of action

Textbook:
Russell and Norvig, Artificial Intelligence, Prentice Hall, 2003

Computer Usage:
Type: Sparcstation running UNIX
Languages: Primarily Prolog, LISP, or C
Proficiency Level Attained: Through four homework assignments and a project where the students design a large artificial intelligence program.

Laboratory Projects:
Homework assignments emphasize basic concepts, with open-ended design components included. Grading based on design as well as grasp of concepts. The programming project involves design of a large artificial intelligence program, using as a basis material presented in lectures. Design involves decisions as to method used and specific application to a knowledge-based real application area.

Engineering Design Statement:
Students must exercise considerable design skills in the project and also in homework assignments. Open-ended choices include choice of methodologies to be used in design and implementation, and evaluation of effectiveness of method used. Examinations also include design-oriented questions.

ABET Category Content:
Engineering Science: 2 units
Engineering Design: 1 unit

Goals:
Students will:

learn non-algorithmic approach to problem solving
learn specific techniques including representing knowledge with first order predicate logic and its extensions, methods of search including heuristic and adversarial, and employing probabilistic reasoning

Program Outcomes:

The ability to work independently
The ability to effectively express ideas through written communication
The ability to apply knowledge of basic science, mathematics and engineering principles to solve computing and information processing problems
The ability to write correct and good programs
The ability to construct appropriate abstractions to manage complexity and to think creatively about new problems
The ability to acquire the foundations to be a life-long learner

Instructors: K. Levitt

Prepared by: K. Levitt (January 1993)

Overlap Statement:
This course does not duplicate any existing course.

5/06

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