Prof. Michael G. Dyer HomePage
CS 161 - Introduction to Natural Language Processing (NLP)
Introduction to fundamental problem solving and knowledge representation paradigms of artificial intelligence. Introduction to LISP. State-space and problem reduction methods, brute-force and heuristic search, planning techniques, two-player games. Knowledge structures including predicate logic, semantic networks. Natural language processing. Special topics in expert systems, vision, neural networks, robotics.
Prerequisites: cs32 (cs130 or cs131 is recommended).
1. Intelligent Agents Approach to AI; Search Strategies and Heuristics.
2. Game Playing: Minimax, Alpha-Beta.
3. Logical Agents:Propositional, First-Order; Knowledge-Based Systems.
4. Automatic Theorem Proving: Unification, Resolution; Logic Programming in Prolog.
5. Partial Order Planning, Hierarchical and Conditional Planning, Replanning.
6. Uncertainty in Knowledge Representation and Probabilistic Reasoning.
7. Utility Theory and Decision Making.
8. Learning from Observations; Learning via Neural Networks; Knoweldge-Based Learning.
9. Natural Language Processing: Syntax & Semantics.
10. Special Topics: Machine Perception, Robotics; Philosophical Issues and Future of AI.
Hours: Lectures: 4 units (meets twice weekly); Lab/Discussion: 2 units (meets every Friday).
Textbook: S. Russell and P. Norvig, 2nd ed., (2002). Artificial Intelligence: A Modern Approach, Prentice-Hall.
Grading: Homework (approx. 50%); Midterm and Final Exams (approx. 50%). Homework includes one or more programming projects.
Offered: cs161 is offered every Fall, Winter, Spring. (Prof. Dyer usually teaches a section of cs161 in the Winter.)