| 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 |
Arfa Hassan |
| 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 o Ethem Alpaydin, Introduction to machine learning, MIT Press, 2004. |
| Course Goals |
Acquire knowledge on intelligent systems and agents, formalization of knowledge, reasoning with and without uncertainty, machine learning and applications at a basic level. |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| Gain an understanding of the key components within the realm of artificial intelligence |
C |
2 |
| Put into practice conventional techniques of artificial intelligence |
C |
3 |
| Examine methods of artificial intelligence for effective problem-solving in practical scenarios. |
C |
4 |
| Cultivate a strong interest in the potential of AI to revolutionize multiple industries and society as a whole |
A |
4 |
| * BT= Bloom’s Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective domain |
|
|
|
| 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. Types of AI, Basic components of AI. |
| Week 2 |
3 |
Artificial agents, types of agents. Agent’s environment, types of environments |
|
4 |
State space graphs and search trees, solving problems by searching, problem solving agents |
| Week 3 |
5 |
Uniformed search |
|
6 |
Informed Search: Greedy search(best first search) |
| Week 4 |
7 |
Informed Search: A* search |
|
8 |
Beyond classical search: Hill-climbing search. |
| Week 5 |
9 |
Beyond classical search: local beam search |
|
10 |
Adversarial Search: Minimax algorithm |
| Week 6 |
11 |
Adversarial Search: Efficiency of Minimax Algorithm, Optimal decisions in games |
|
12 |
Adversarial Search: alpha-beta pruning |
| Week 7 |
13 |
Introduction to fuzzy logic |
|
14 |
Uncertainty in Fuzzy logic |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Type I Fuzzy Logic |
|
16 |
Type II Fuzzy Logic |
| Week 10 |
17 |
Constraint satisfaction problem: Part I |
|
18 |
Constraint satisfaction problem: Part II |
| Week 11 |
19 |
Logical agents: Knowledge-based agents |
|
20 |
Logical agents: Propositional logic |
| Week 12 |
21 |
First-order logic: representation revisited, Knowledge engineering in first-order logic |
|
22 |
First-order logic: Knowledge engineering in first-order logic |
| Week 13 |
23 |
Neural Networks |
|
24 |
Learning: Regression and Classification |
| Week 14 |
25 |
Unsupervised Learning |
|
26 |
Reinforcement Learning |
| Week 15 |
27 |
Natural Language Processing |
|
28 |
AI Application In Digital Image Processing |
| Week 16 |
29 |
Advance Applications of AI Algorithm |
|
30 |
Case Study |
| Week 17 |
2 hours |
Final Term |
|
| Laboratory Projects/Experiments Done in the Course |
Python Programming Language Basics, Solving search problems (Depth-First Search, Breadth-First, A*), Fuzzy logic type and applications, Learning: supervised learning (regression and classification), Unsupervised Learning (Clustering), Machine learning applications. |
| Programming Assignments Done in the Course |
Lab Project |
