CS3243
INTRODUCTION TO ARTIFICIAL INTELLIGENCE (2010/2011, Semester 2) 

 MODULE OUTLINE Created: 10-Jan-2011, Updated: 10-Jan-2011
 
Module Code CS3243
Module Title INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Semester Semester 2, 2010/2011
Modular Credits 4
Faculty School of Computing
Department Computer Science
Timetable Timetable/Teaching Staff
Module Facilitators
DR Low Bryan Kian Hsiang Lecturer
Consultation : By appointment only.
YU JIANGBO Tutor
CHEN JIE Tutor
Weblinks
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Learning Outcomes | Prerequisites | Schedule | Syllabus | Assessment | Preclusions | Workload


 LEARNING OUTCOMES Top
The module introduces the basic concepts in search and knowledge representation as well as to a number of sub-areas of artificial intelligence. It focuses on covering the essential concepts in AI. The module covers intelligent agents; uninformed/blind search: breadth-first search, uniform-cost search, depth-first search, depth-limited search, iterative deepening search; informed/heuristic search: greedy best-first search, A* algorithm; local search: hill climbing and simulated annealing; adversarial search: minimax algorithm and alpha-beta pruning; constraint satisfaction problems: backtracking search, constraint propagation, local search; logical agents: propositional logic, first-order logic, logical inference; uncertainty: Bayes’ rule, Bayesian inference, independence and conditional independence, Bayesian networks; machine learning: decision tree learning, naive Bayes classifier.


 PREREQUISITES Top
(CS1102 or CS1102C or CS1102S) and CS1231


 SCHEDULE Top
Lectures: Mon 1600 - 1800 LT19


 SYLLABUS Top

Week

Lecture Topics

Remarks

1     (10/01)

Introduction and Intelligent Agents

 

2     (17/01)

Uninformed Search

 

3     (24/01)

Informed Search

 

4     (31/01)

Adversarial Search

 

5     (07/02)

Constraint Satisfaction

 

6     (14/02)

Logical Agents (Part 1)

 

TERM BREAK (Sat 19 Feb – Sun 27 Feb 2011)

7     (28/02)

MIDTERM EXAM

 

8     (07/03)

Logical Agents (Part 2)

 

9     (14/03)

First-Order Logic

 

10   (21/03)

Logical Inference

 

11   (28/03)

Uncertainty

 

12   (04/04)

Machine Learning

 

13   (11/04)

Exam Revision

 

FINAL EXAM

Monday, 25 April 2011 (Morning)

 


 ASSESSMENT Top
Class participation + Homework Assignments 30%
Midterm Exam 20%
Final Exam 50%
 


 PRECLUSIONS Top
EEE and CPE students can only take this module as a technical elective to satisfy the program requirements or UEM but not CFM/ULR-Breadth.


 WORKLOAD Top
2-1-0-3-4