Course Description

COURSE DESCRIPTION

NAME OF INSTITUTION	Lahore Garrison University
PROGRAM (S) TO BE EVALUATED	Computer Science , Spring 2022

Course Description :

Course Code

CSC363

Course Title

Artificial Intelligence

Credit Hours

3+1

Prerequisites by Course(s) and Topics

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

URL (if any)

Current Catalog Description

Textbook (or Laboratory Manual for Laboratory Courses)

Reference Material

Course Goals

Course Learning Outcomes (CLOs):

At the end of the course the students will be able to:	Domain	BT Level*
1-Understand historical perspective of AI and its foundations.	BT	2
Understand the basic principles of AI toward problem solving, inference, perception, knowledge representation, and learning.	BT	2
Implement classical and local search techniques as well as self- learning algorithms.	BT	3
Analyze the outcomes of classical and local search techniques as well as self-learning algorithms to specific set of problems.	BT	4
Evaluate the performance of AI techniques under particular problem domain.	5
* 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.
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: Depth-First search with examples.
	8	Uniformed search: Breadth-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.
Week 7	13	Beyond classical search: random restart hill climb search, Simulated annealing.
	14	Beyond classical search: local beam search.
Week 8	1 hours	Mid Term
Week 9	15	Beyond classical search: Genetic algorithm.
	16	Revision.
Week 10	17	Adversarial Search: Minimax algorithm.
	18	Adversarial Search: Efficiency of Minimax Algorithm, Optimal decisions in games.
Week 11	19	Adversarial Search: alpha-beta pruning.
	20	Constraint satisfaction problem: Part I.
Week 12	21	Constraint satisfaction problems: Part II
	22	Logical agents: Knowledge-based agents
Week 13	23	Logical agents: Propositional logic
	24	Logical agents: Forward chaining
Week 14	25	Logical agents: Backward chaining
	26	First-order logic: representation revisited, Knowledge engineering in first-order logic
Week 15	27	First-order logic: Knowledge engineering in first-order logic
	28	Learning: Regression and Classification
Week 16	29	Learning from examples: Artificial Neural Networks
	30	Advanced Applications
Week 17	2 hours	Final Term

Laboratory Projects/Experiments Done in the Course

Programming Assignments Done in the Course

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Instructor Signature
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