Download HERE - Faculty of Computing

COURSE OUTLINE
Department & Faculty: Dept. of Software Engineering,
Faculty of Computing
Course Code: SCSJ3553
Course Name : Artificial Intelligence
Total Contact Hours: 42 hours
Course Pre-requisite: None
Page : 1 of 6
Semester: 1
Academic Session: 2014/2015
Lecturer
:
AP. Dr Siti Zaiton Mohd Hashim
Room No.
:
Academic Office, N28a
Telephone No.
:
0197726248
E-mail
:
[email protected]
Class Hours
Synopsis
Sunday(KL)
:
This course offers students a new perspective on the study of Artificial
Intelligence (AI) concepts. The essential topics and theory of AI are presented,
but it also includes practical information on data input and reduction as well as
data output (i.e. algorithm usage). In particular, this course emphasises on
theoretical and practical aspects of various search algorithms, knowledge
representations, and machine learning methods. The course features practical
implementations through assignments undertaken both individually and in groups.
LEARNING OUTCOMES
By the end of the course, students should be able to:
Course Learning Outcome
No.
1.
Programme
Learning
Outcome(s)
Addressed
Assessment
Methods
Explain the basic definition and concept of AI.
PO1(C2,A3,
P2)
Q, T, F
Identify the types of AI techniques and understand
the role of search algorithms, knowledge
representation, and machine learning methods.
PO1(C3,A3)
A, Q, T, F
3.
Formulate appropriate solutions for problems and
design intelligent computer-based systems..
PO1(C3,A3,P1)
PO2(C3,P2,
A3)
A, Q, PR, Pr
4.
Develop teamworking skills for implementing AI
techniques in real-world problems.
PO5(CTPS –
CTPS3)
PO6 (TS1 –
TS3)
A, PR, Pr
2.
(T – Test ; PR – Project ; A – Assignment ; Q-Quiz, Pr
– Presentation, F – Final Exam)
Updated by:
Name: Dr. Afnizanfaizal Abdullah
Signature:
Date: 31 August 2014
Certified by: Name:
Signature:
Date:
COURSE OUTLINE
Department & Faculty: Dept. of Software Engineering,
Faculty of Computing
Course Code: SCSB 3553
Course Name : Artificial Intelligence
Total Contact Hours: 42 hours
Course Pre-requisite: None
Page : 2 of 6
Semester: 1
Academic Session: 2012/2013
STUDENT LEARNING TIME
Teaching and Learning Activities
Face to face Learning

Lecturer Centered

Student Centered

Others
Lecture
- Practical/Lab/Tutorial
- Student Centered
Activity
Student Learning
Time (hours)
38
4
0
0
Sub Total
Self Learning
Formal Assessment
42

Non Face to face or Student Centered
Learning (SCL)



Revision
Assessment Preparation
Others
Sub Total
25
12
0

Continuous Assessment
5


Final Examination
Others
Sub Total
3
0
33
70
TOTAL SLT
TEACHING METHODOLOGY
Lecture and Discussion, Co-operative Learning, Independent Study, Group Project, Presentation
8
120
COURSE OUTLINE
Department & Faculty: Dept. of Software Engineering,
Faculty of Computing
Course Code: SCSB 3553
Course Name : Artificial Intelligence
Total Contact Hours: 42 hours
Course Pre-requisite: None
Page : 3 of 6
Semester: 1
Academic Session: 2012/2013
Week
Topics
Activities/hours
Week 1
(7-12/9)
1.0 Introduction to Computers and Programming
1.1 The overview and history of AI
1.2 Why study AI?
1.3 AI application areas
Lecture : 3
Week 2
*(14-19/9)
*16/9
Malaysia Day
2.0
2.1
2.2
2.3
2.4
Knowledge Representation and Search
Propositional calculus
Predicate calculus
First order Predicate Calculus
Syntax and Semantic
Lecture : 3
Week 3
(21-26/9)
3.0
3.1
3.2
3.3
Knowledge Representation and Search (cont..)
Inference Process
Unification
Proof Procedure
Lecture : 3
Week 4
(28/9-3/10)
4.0 Introduction to AI Programming
4.1 PROLOG as AI Programming Language
Lecture : 1
Lab : 2
Week 5
(5-10/10)
*5/10 Eid AlAdha
5.0 Problem Solving Using Search
5.1 Graph theory
5.2 Structures for state space
5.3 Problem representation in state space search
5.4 Evaluation Criteria
5.5 Strategy for state space search
5.5.1
Goal driven
5.5.2
Data driven
5.6 Implementation of search Graph
Lecture : 3
Week 6
*(12-17/10)
6.0 Exhaustive Search Algorithm
6.1 Backtracking
6.2 Breadth-first search
6.3 Depth-first search
Lecture : 3
Assessment:
Quiz 1
Assessment:
Assignment 1
Assessment:
Quiz 2
Mid-Term Test
Oct 14, 2014
MID SEMESTER BREAK (19-25/10)
Week 7
(26-30/10)
7.0 Heuristic Search
7.1 Heuristic search algorithm
7.11 Heuristic Search Strategy
7.12 Heuristic Evaluation Function
Lecture : 3
Week 8
(2-7/11)
*2/11
Deepavali
8.0
8.1
8.2
8.3
Lecture : 3
Week 10
9.0 Heuristic in Game Playing
Searching Using Heuristic Algorithm
Best-first search
A* Search
Criteria to evaluate Heuristic
8.3.1 Admissibility, Monotonicity and Informedness
Assessment:
Quiz 3
Lecture : 3
COURSE OUTLINE
Department & Faculty: Dept. of Software Engineering,
Faculty of Computing
Course Code: SCSB 3553
Course Name : Artificial Intelligence
Total Contact Hours: 42 hours
Course Pre-requisite: None
(9-14/11)
Page : 4 of 6
Semester: 1
Academic Session: 2012/2013
9.1 Minimax Search
9.2 Alpha-Beta Search
Week 11
*(16-21/11)
*22/11 B’day
Sultan Johor
10.0
10.1
10.2
10.3
Building Control Algorithms for State Space Search
Recursion-based search
Production Systems
Blackboard architecture
Week 12
(23-28/11)
11.0
11.1
11.2
11.3
11.4
11.5
Knowledge Representation (KR)
Issues in Knowledge Representation
Semantic Network
Frames
Conceptual Graph
Agent-Based and Distributed Problem Solving
12.0
12.1
12.2
12.3
12.4
12.5
Machine Learning
What is Machine Learning?
The principles of Machine Learning
The types of Machine Learning
Classification, Clustering, and Optimization
Application to the real-world problems
Week 13
(30/115/12)
Week 14
(7-12/12)
Lecture : 3
Assessment:
Quiz 4
Lecture : 3
Assessment:
Assignment 3
Lecture : 3
Assessment:
Project Presentation
STUDY WEEK
Week 15-17
(14-19/12)
(21-25/12)
(28/121/1/2015)
REFERENCES
Assessment:
Assignment 2
EXAMINATION WEEK
:
Main Text:
1. Luger, G.F & Stubblefield, W.A, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th
Edition, Addison-Wesley, 2009.
Other References:
1. Rich, E & Knight, K, Artificial Intelligence, 2nd Edition, McGraw-Hill Publication, 1991.
2. Dean et. al, Artificial Intelligence: Theory and Practice, The Benjamin Cummings Publishing Co, Inc, 1995.
3. Schalkoff, R.J., Artificial Intelligence: An Engineering Approach, McGraw-Hill Publication, 1990.
4. Winston, P.H., Artificial Intelligence, 3rd Edition, Addison-Wesley Publishing Co, 1993.
COURSE OUTLINE
Department & Faculty: Dept. of Software Engineering,
Faculty of Computing
Course Code: SCSB 3553
Course Name : Artificial Intelligence
Total Contact Hours: 42 hours
Course Pre-requisite: None
Page : 5 of 6
Semester: 1
Academic Session: 2012/2013
GRADING
No.
Assessment
Number
% each
% total
1
Assignments
3 (minimum)
5%
15
2
Harvard Bussiness
School (HBS) Case
Study
1
5%
5
3
Quizzes
4 (minimum)
2.5%
10
4
Project
1
10%
10
5
Presentation
1
5%
5
6
Mid-term exam
1
15%
15
7
Final Exam
1
40%
40
Overall Total
Dates
100
ASSESSMENT DISTRIBUTION BASED ON COURSE LEARNING OUTCOMES (CLO)
Course Learning Outcome (CLO) in %
Full
Distribution
No
Assessment
Mark
%
CLO 1
CLO 2
CLO 3
CLO 4
Total
%
1
Test 1 - Part A
15
2.25
20
80
100
2
Test 1 - Part B
20
3.00
20
80
100
3
Test 1 - Part C
65
9.75
4
Final Exam - Part A
15
6.00
5
Final Exam - Part B
20
8.00
6
Final Exam - Part C
65
26.00
7
Assignment 1
5
5.00
8
Assignment 2
5
5.00
70
9
Assignment 3
5
5.00
40
10
Quiz 1
2.5
2.50
80
20
100
11
Quiz 2
2.5
2.50
80
20
100
12
Quiz 3
2.5
2.50
80
20
100
13
Quiz 4
2.5
2.50
80
20
100
14
Group Project 1
10
10
50
50
100
15
Presentation
5
5
50
50
100
16
HBS Case Study
5
Total
5
100
50
36.45*
50
11.5*
100
100
100
20
80
20
11.25*
* Sum product of CLO (in %) and Distribution (in %) over 100%
100
80
100
100
20
100
10
100
20
10
100
50
10
100
70
40.80*
100
COURSE OUTLINE
Department & Faculty: Dept. of Software Engineering,
Faculty of Computing
Course Code: SCSB 3553
Course Name : Artificial Intelligence
Total Contact Hours: 42 hours
Course Pre-requisite: None
Page : 6 of 6
Semester: 1
Academic Session: 2012/2013
COURSE POLICY
1. Attendance is compulsory and will be taken in every lecture session. Students with less than 80% total
attendance are not allowed to sit for final exam.
2. Students are required to behave and follow the dressing regulation and etiquette which has been stated
in University ruling while in class, in lab, and in exam hall.
3. Any form of plagiarisms is NOT ALLOWED. Students who are caught cheating during exams may FAIL
the course (no mark for cheating during Quiz). Students who copied other student’s assignment/lab
exercise will get zero mark.
4. Exercises will be given in class and some may be taken for assessment. Students who do not take the
exercise will lose the coursework marks for the exercise.
5. Make up exam will not be given, except to students who are sick and submit medical certificate which is
confirmed by UTM panel doctors. Make up exam can only be given within one week from the initial date
of exam.
6. Assignments must be submitted on the due dates. Some points will be deducted for the late submission.
Assignments that are hand over after three days from the due dates will not be accepted.