ECS 170 - AI

Course Outline


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)

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

      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

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

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)

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


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.)

Reasoning with Bayes Nets

Na´ve Bayes
Homework Assignment #5a. probabilistic reasoning

Homework Assignment #5b. Bayes Learning



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