| Course Code |
CSC363 |
| Course Title |
Artificial Intelligence |
| Credit Hours |
3+1 |
| Prerequisites by Course(s) and Topics |
Object Oriented Programming |
| 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. Sadaf Hussain |
| URL (if any) |
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| Current Catalog Description |
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| Textbook (or Laboratory Manual for Laboratory Courses) |
Russell, S. and Norvig, P. “Artificial Intelligence. A Modern Approach”, 3rd ed, Prentice, Hall, Inc., 2015 |
| Reference Material |
Norvig, P., “Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp”, Morgan Kaufman Publishers, Inc., 1992. Luger, G.F. and Stubblefield, W.A., “AI algorithms, data structures, and idioms in Prolog, Lisp, and Java”, Pearson Addison-Wesley. 2009. Severance, C.R., 2016. “Python for everybody: Exploring data using Python 3.” CreateSpace Independent Publ Platform. Miller, B.N., Ranum, D.L. and Anderson, J., 2019. “Python programming in context.” Jones & Bartlett Pub. Joshi, P., 2017. “Artificial intelligence with python.” Packt Publishing Ltd |
| Course Goals |
The major goals of artificial intelligence correspond to understanding the core concepts of intelligence in artifacts. It also includes understanding knowledge representation, reasoning, planning, machine learning, natural language processing, computer vision, and robotics. |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| Explain the concepts, applications, skills, and knowledge required for AI systems. |
C |
2 |
| Apply Artificial Intelligence techniques for problem-solving. |
C |
3 |
| Display the use of classical Artificial Intelligence techniques. |
P |
2 |
| Infer the domains of AI in machine learning, natural language processing, computer vision, and robotics. |
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 |
Definition of AI, approaches to AI, Foundations of AI, History of AI |
|
2 |
Basic component of AI, Identifying AI systems, branches of AI, etc. types of problems addressed. |
| Week 2 |
3 |
Discuss various schools of thoughts on artificial intelligence including weak artificial intelligence, strong artificial intelligence, and neat artificial intelligence. |
|
4 |
Applications and Success stories on artificial Intelligence. Discuss the technological singularity and role of artificial intelligence. |
| Week 3 |
5 |
Good behavior: the concept of rationality, The nature of environments |
|
6 |
Structure of agents, agent programs, simple reflex agents, model-based reflex agents, goal-based agents, |
| Week 4 |
7 |
utility-based agent, learning agents, how the components of agent programs work |
|
8 |
Problem-solving agents, problem formulation, example problems, searching for solutions, Uninformed searching: Breadth-first search, Depth-first search |
| Week 5 |
9 |
Depth-limited search, Iterative deepening depth-first search, Bidirectional search |
|
10 |
Informed searching: Greedy best-first search, A* search, Memory-bounded heuristic search |
| Week 6 |
11 |
Heuristics, Local searching, Min-max algorithm, Alpha beta pruning, Game-playing |
|
12 |
Hill climbing algorithm and its variations. Constraint satisfaction problems. |
| Week 7 |
13 |
Genetic algorithms |
|
14 |
Belief Networks. Bayesian theorem and its applications. Uncertainty in environment and possibilities for designing intelligent agents that can work in uncertain environments |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Knowledge Representation Schemas: Logic, propositional logic, first-order logic |
|
16 |
Knowledge Representation: frames, semantic nets, scripts. knowledge graphs |
| Week 10 |
17 |
|
|
18 |
|
| Week 11 |
19 |
Reasoning in logic programming: unification, horn clause logic, and resolution, fuzzy modeling based expert system design and implementation. |
|
20 |
Problems in knowledge representation. Expert systems |
| Week 12 |
21 |
Case Studies: General Problem Solver, Eliza |
|
22 |
Case Studies: Student, Macsyma |
| Week 13 |
23 |
Machine Learning: Introduction, unsupervised learning, supervised learning, reinforcement learning, decision trees |
|
24 |
Bayesian classification, artificial neural networks. |
| Week 14 |
25 |
Methods to implement machine learning. Neural Networks. Structure and working of human brain. Supervised learning algorithms. |
|
26 |
Language Models (N-Gram Character Models, Smoothing N-Gram Models, Model Evaluation, N-Gram Word Models), |
| Week 15 |
27 |
Text Classification, Information Retrieval, Information Extraction |
|
28 |
Phrase Structure Grammars, Parsing, Augmented Grammars and Semantic Interpretation, |
| Week 16 |
29 |
Machine Translation, Speech Recognition |
|
30 |
Image processing, perception generation (object recognition), vision |
| 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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