ARTIFICIAL INTELLIGENCE-SPACE

2014/2015

SEMESTER 1

ARTIFICIAL INTELLIGENCE-SPACE KL

Lecturer : PM Dr Siti Zaiton Mohd Hashim
Room No. : Academic Office, Level 3, N28a
Telephone No. : 0197726248
E-mail : sitizaiton@utm.my
Class Hours : SPACE KL -AI
Synopsis : 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 Outcame : –
Reminders :
  • ATTENDANCE is compulsory. Students with less than 80% attendance will be prohibited from sitting the final exam.
  • Students MUST DRESS respectably (SOPAN in malay) when attending the lecture, please follow the dress code required.
  • Students must abide by the rules set by University at all times in the lectures, laboratories and exams.
  • CHEATING in any form is PROHIBITED. Gred F is automatically given if the student is caught cheating during the exam. Zero mark is given if the student is found copying/plagiarizing other’s work.
  • Quizzes and EXAMs ARE NOT REDONE unless the student is sick and certified by UTM Medical panels, to be done within a week after the exam.
  • LATE assignment will be penalized or rejected.
 -PLEASE DOWNLOAD COURSE SYLLABUS HERE (Updated On 15 Jan 2015)

Week Topics Activities/hours
Week 1               
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
2.0 Knowledge Representation and Search
2.1  Propositional calculus
2.2 Predicate calculus
2.3 First order Predicate Calculus
2.4 Syntax and Semantic		 Lecture : 3
Assessment:Quiz 1
Week 3  
3.0 Knowledge Representation and Search (cont..)
3.1  Inference Process
3.2  Unification
3.3  Proof  Procedure		 Lecture : 3
Assessment:Assignment 1
Week 4
4.0    Introduction to AI Programming
4.1 PROLOG as AI Programming Language		 Lecture : 1
Lab : 2
Week 5
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
Assessment:TakeHomeQuiz 2
Week 6
6.0   Exhaustive Search Algorithm
6.1   Backtracking
6.2   Breadth-first search
6.3   Depth-first search		 Lecture : 3 
Assessment: Assignment 2
Week 7
7.0    Heuristic Search
7.1   Heuristic search algorithm
7.11  Heuristic Search Strategy
7.12  Heuristic Evaluation Function		 Lecture : 3
Assessment:TEST
Week  8
8.0    Searching Using Heuristic Algorithm
8.1   Best-first search
8.2   A* Search
8.3   Criteria to evaluate Heuristic
8.3.1 Admissibility, Monotonicity and Informedness 		 Lecture : 3
Assessment:Nil 
Week  9 
9.0   Heuristic in Game Playing
9.1   Minimax Search
9.2   Alpha-Beta Search		 Lecture : 3
Assessment:Assignment 3
Week  10
10.0Building Control Algorithms for State Space Search
10.1    Recursion-based search
10.2    Production Systems
10.3    Blackboard architecture		 Lecture : 3
Assessment:Quiz 3
Week 11
11.0Knowledge Representation (KR)
11.1    Issues in Knowledge Representation
11.2    Semantic Network
11.3    Frames
11.4    Conceptual Graph
11.5    Agent-Based and Distributed Problem Solving 		 Lecture : 3
Assessment:Nil
Week 12- 13
12.0    Strong Method Problem Solving
12.1    Introduction to expert system
12.2    Rule-based Expert System
12.3    Case-based, model-based & hybrid expert systems 		 Lecture : 3
Assessment:Quiz 4
Week 14
15.0   Computational Intelligent
15.1   Introduction to Computational Intelligent
15.2   Soft Computing 		 Lecture : 3
Assessment:Project Assignment
Week 15 STUDY WEEK   EXAMINATION WEEK