**General Advise: Please read all the
reading assignments. You learn a number of things that I cannot possibly cover.
You can skip topics that are NOT listed AND not covered in the class. You
are responsible for everything that is on the Reading List OR that is covered
in the class (or both).**

**Topics to be Covered and Approximate Schedule: (Subject to revision)**

- 1.
**Introduction**

(a) Introduction to AI -Read Chapters 1-2 (1 Lecture)

History.

Central Issues.

Lecture#1 – History (Notes) [html]

Lecture#2 – Intro to AI
(Notes) [html]

Glossary of AI
terms

(b) Introduction
to** LISP** (1 discussion session by TA, No
lecture on this topic)

Homework Assignment #0. Focus on formulation, state
spaces, and review of LISP

- 2.
**Problem Solving by Search (4 Lectures)**

This topic covers three important
categories in Searching: Blind Search, Heuristic Search, Adversarial Search

Read Chapter 3. Sections 3.1 thru 3.6 (skip bidirectional search), Section 3.8

Read Chapter 4. Sections 4.1, 4.2 and 4.5

- How to write a LISP program for Depth First Search?
- Search Slides – Part 1, Notation [ppt]
- Blind Search Slides – Part 2 [ppt]
- Blind Search Slides – Part 3 [ppt]
- Lecture #3 notes [html]
- Animations of Blind Search Methods –
- Heuristic Search Notes [html]
- Heuristic Search Animations [ppt]
- Programming Assignment # 1. (Focus is on using LISP and solve a Search Problem) (3 weeks time)
- Homework Assignment #1. Focus on search methods
- Adversarial search [notes] [ppt] [pdf]
- Homework Assignment #2.
Focus on Adversarial Search

Read Chapter 5, Sections 5.1 thru 5.4 and 5.8

- 3.
**Knowledge Representation**(3 lectures)

This part covers the very broad area of what knowledge is and some of the
numerous methods of solving logic problems. The main methods covered are
Propositional Logic (very quick survey), First Order Predicate Logic and Rule
based systems.

Knowledge Representation and Logic [html] [ppt]

Propositional logic [pdf] [ppt]

Read Chapter 6.

Predicate or First Order Logic- [pdf] [ppt]

Validity and inference, models and entailment, rules of inference, Resolution

Read Chapter 7.1 thru 7.5. (skip 7.6 thru 7.9)

Read Chapter 9. Sections 9.1 thru 9.3; 9.5 thru 9.8

Homework
Assignment #3. Focus on Logic

**Midterm. 6 February (in
class room, open textbook)**

**4. Learning (4 lectures)**

Learning in neural networks: Perceptrons

Homework Assignment #4a Focus on Perceptron Learning

Error back propagation (BP) in feed
forward networks

Read Chapter 19

Sample
Backprop code in Matlab

Programming
Assignment #2. Focus on BP Learning

Decision
tree learning. ID3, C4.5

Read Chapter 18.1 thru 18.5

Homework
Assignment #4b Focus on Decision
Trees

- 5.
**Probabilistic Reasoning and Decision Making (3 lectures)**

Read Chapters 14, 15 and 16

Basic probability concepts [pdf], conditional
probability. Baye's Theorem

A popular intro to Bayesian Mehods?
[NY Times article]
[Economist article] [Clinical
trials]

Joint probability distributions.

Bayesian Networks without Tears [pdf]

More on Bayes Nets (or, Belief networks.)

Naïve
Bayes

Homework
Assignment #5a. probabilistic reasoning

Homework Assignment #5b. Bayes
Learning

** **

**Summary**

**Final Examination**
(covers the entire course)

Department of Computer Science

University of California at Davis

Davis, CA 95616-8562

Page last modified on 2/21/2003