CS3243
INTRODUCTION TO ARTIFICIAL INTELLIGENCE (2012/2013, Semester 2) 

 MODULE OUTLINE Created: 12-Dec-2012, Updated: 12-Dec-2012
 
Module Code CS3243
Module Title INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Semester Semester 2, 2012/2013
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.
XU NUO Tutor
HOANG TRONG NGHIA 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
(CS2010 or its equivalent) and (CS1231 or MA1100).


 SCHEDULE Top
Lectures: Fri 1200-1400 LT19


 SYLLABUS Top

Week

Lecture Topics

Remarks

1     (18/01)

Introduction and Intelligent Agents

 

2     (25/01)

Uninformed Search

 

3     (01/02)

Informed Search

 

4     (08/02)

Adversarial Search

 

5     (15/02)

Constraint Satisfaction

 

6     (22/02)

Logical Agents (Part 1)

 

TERM BREAK (Sat 23 Feb – Sun 3 Mar 2013)

7     (08/03)

MIDTERM EXAM

 

8     (15/03)

Logical Agents (Part 2)

 

9     (22/03)

First-Order Logic

 

10   (29/03)

Logical Inference

Good Friday (rescheduled to 28 Mar 4-6pm in LT15)

11   (05/04)

Uncertainty

 

12   (12/04)

Machine Learning

 

13   (19/04)

Exam Revision

 

FINAL EXAM

Thursday, 2 May 2013 (Morning)

 



 ASSESSMENT Top
Class participation + Homework Assignments + Term Project 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

Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week