| Course Code |
CSC363 |
| Course Title |
Artificial Intelligence |
| Credit Hours |
3+1 |
| Prerequisites by Course(s) and Topics |
Data Structures and Algorithms |
| Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.) |
SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%
|
| Course Coordinator |
Dr. Umer Farooq |
| URL (if any) |
|
| Current Catalog Description |
|
| Textbook (or Laboratory Manual for Laboratory Courses) |
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, 3rd Edition, Prentice Hall, Inc.2010. |
| Reference Material |
1) Simon Haykin, Neural Networks: a comprehensive foundation, 2nd ed., Prentice Hall, 1999. 2)Bishop, Christopher, Neural Networks for Pattern Recognition, Oxford University Press, 1995. 3)Ethem Alpaydin, Introduction to machine learning, MIT Press, 2004. |
| Course Goals |
This course is to provide a combined applied and theoretical background in Artificial Intelligence to improve students’ understanding: 1) Expert Systems 2) Logic 3) Artificial agent searching environment 4)Problem-solving using AI techniques |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| Explain what constitutes "Artificial" Intelligence and how to identify systems with Artificial Intelligence. |
C |
2 |
| Identify the capabilities of Artificial Intelligent system beyond conventional technology. |
A |
2 |
| Implement Artificial Intelligence techniques for problem solving and searching |
A |
3 |
| Evaluate Machine Learning algorithms and implement for industrial problems |
C |
4 |
| * BT= Bloom’s Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective domain |
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| Topics Covered in the Course, with Number of Lectures on Each Topic (assume 15-week instruction and one-hour lectures) |
| Week | Lecture | Topics Covered |
|---|
| Week 1 |
1 |
Introduction to Artificial Intelligence. |
|
2 |
Foundations and history of AI |
| Week 2 |
3 |
Types of AI, Basic components of AI |
|
4 |
Artificial agents, types of agents |
| Week 3 |
5 |
Agent’s environment, types of environments. |
|
6 |
State space graphs and search trees, solving problems by searching, problem solving agents. |
| Week 4 |
7 |
Uniformed search: Breadth -First search with examples. |
|
8 |
Uniformed search: Depth -First search with examples. |
| Week 5 |
9 |
Uniformed search: Uniform Cost with examples. |
|
10 |
Informed Search: Greedy search. |
| Week 6 |
11 |
Informed Search: A* search. |
|
12 |
Beyond classical search: Hill-climbing search, random restart hill climb search |
| Week 7 |
13 |
Beyond classical search: Simulated annealing |
|
14 |
Revision |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Beyond classical search: local beam search |
|
16 |
Beyond classical search: Genetic algorithms |
| Week 10 |
17 |
Mid Term |
|
18 |
Mid Term |
| Week 11 |
19 |
Adversarial Search: Minimax algorithm |
|
20 |
Adversarial Search: Efficiency of Minimax Algorithm, Optimal decisions in games |
| Week 12 |
21 |
Adversarial Search: alpha-beta pruning |
|
22 |
Constraint satisfaction problem: Part I |
| Week 13 |
23 |
Constraint satisfaction problem: Part II |
|
24 |
Logical agents: Knowledge-based agents |
| Week 14 |
25 |
Logical agents: Propositional logic |
|
26 |
Logical agents: Forward chaining |
| Week 15 |
27 |
Logical agents: Backward chaining |
|
28 |
First-order logic: representation revisited, Knowledge engineering in first-order logic |
| Week 16 |
29 |
Learning: Regression and Classification |
|
30 |
Learning from examples: Artificial Neural Networks |
| Week 17 |
2 hours |
Final Term |
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| Laboratory Projects/Experiments Done in the Course |
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| Programming Assignments Done in the Course |
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