First Cycle - Faculty of Engineering - Computer Engineering (English)
Y : Year of Study S : Semester
Course Unit Code Course Unit Title Type of Course Y S ECTS
CSE4082 Artificial Intelligence Compulsory 4 7 5
Objectives of the Course
The main objective of the course is to teach basic methods in AI, and to arouse interest for the field. Students passing this course are expected to be able to analyze problems, apply AI techniques for solving the problems, and conduct AI based researches.
Learning Outcomes
1 employ critical-thinking skills to develop intelligent frameworks for problem solving
2 employ critical-thinking skills to relate computational to natural intelligence
3 select among a range of techniques for the implementation of intelligent systems
4 assess the applicability of AI techniques in novel domains
5 assess the validity of approaches to model intelligent processing
6 assess the claims of AI practitioners as they relate to `intelligence'
7 appreciate the difficulty of distinguishing AI from advanced computer science in general
8 demonstrate knowledge and understanding of some of the principal achievements and shortcomings of AI
Mode of Delivery
Formal Education
Recommended Optional Programme Components
None
Course Contents
Introduction to Artificial Intelligence, Intelligent Agents, Uninformed Search, Informed Search and Heuristics, Prolog Programming, Knowledge Representation and Expert Systems, Planning.
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1 Introduction to Artificial Intelligence
2 Intelligent Agents
3 Uninformed Search
4 Uninformed Search
5 Informed Search and Heuristics
6 Informed Search and Heuristics
7 PROLOG Programming - Syntax and Meaning of PROLOG Programs
8 Midterm Exam
9 PROLOG Programming - Lists, Operators and Arithmetic
10 PROLOG Programming - Using Structures
11 PROLOG Programming - Controlling Backtracking
12 Game Playing
13 Knowledge Representation and Expert Systems
14 Knowledge Representation and Expert Systems
15 Planning
16 Final Exam Study
17 Final Exam
Recommended or Required Reading
Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Pearson, 3rd ed., 2010.
Ivan Bratko, PROLOG Programming for Artificial Intelligence, 4th ed., 2011.
Planned Learning Activities and Teaching Methods
Lecture notes, slides, homeworks and projects, exams
Assessment
AssessmentQuantityWeight
Term (or Year) Learning Activities60
End Of Term (or Year) Learning Activities40
Total100
Term (or Year) Learning ActivitiesQuantityWeight
Midterm Exam150
Project350
Total100
End Of Term (or Year) Learning ActivitiesQuantityWeight
Final Exam1100
Total100
Language of Instruction
Language Codes
Work Placement(s)
None
Workload Calculation
Activities Number Time (hours) Total Work Load (hours)
Theoretical 14 3 42
Midterm Preparation 1 8 8
Final Preparation 1 15 15
Project 3 20 60
Total 19 46 125
Contribution of Learning Outcomes to Programme Outcomes
PO 1PO 2PO 3PO 4PO 5PO 6PO 7PO 8PO 9PO 10PO 11PO 12PO 13PO 14PO 15PO 16
LO 10000000000000000
LO 20000000000000000
LO 30000000000000000
LO 40000000000000000
LO 50000000000000000

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