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Muhammad Ali Jinnah University, Karachi
(COURSE OUTLINE)
Program
Semester
Course
Course Code
Prerequisite(s)
BS(CS)
Spring 2017
Artificial Intelligence
CS5803
1. Computer Programming
2. Discrete Mathematics
3. Probability & Statistics
Course Instructors
Text Book
Ahsan Ghazi
Russell, Stuart and Peter Norvig, Artificial
Intelligence: A Modern Approach (AIMA), 3rd
edition, Prentice-Hall, New Jersey, 2010. ISBN 013-604259-7
Course Description
This course is a broad graduate level introduction to the field of artificial intelligence (AI).
Topics covered will include state-based problem solving, heuristic (informed) search,
constraint satisfaction algorithms, game playing algorithms, propositional and first-order
logic, logical inference algorithms, representations of uncertainty, optimal decision making,
Bayesian networks, and basic principles of machine learning, Pointers to real-world
applications in areas such as computer vision, speech recognition, robotics, etc., will be used
as appropriate to illustrate various concepts.
Course Learning Outcomes:
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Upon successful completion of this course, the student shall be able to:
Demonstrate fundamental understanding of the history of artificial intelligence (AI)
and its foundations.
Understand different types of AI agents.
Know various AI search algorithms (uninformed, informed, heuristic, constraint
satisfaction, genetic algorithms).
Understand the fundamentals of knowledge representation (logic-based, frame-based,
semantic nets), inference and theorem proving.
Know how to build simple knowledge-based systems.
Demonstrate working knowledge of reasoning in the presence of incomplete and/or
uncertain information.
Ability to apply knowledge representation, reasoning, and machine learning
techniques to real-world problems.
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Demonstrate an ability to share in discussions of AI, its current scope and limitations,
and societal implications.
Topics
Reading Assignments
1
Introduction to Artificial Intelligence
AIMA, Chapter 1
2.
State Space Search
AIMA, Chapter 2& 3
3.
Uninformed Search
AIMA, Chapter 3
4.
Heuristic Search
AIMA, Chapter 4
5.
Local Search
AIMA, Chapter 4
6.
Adversarial Search
AIMA, Chapter 5
7.
8.
Constraint Satisfaction
AIMA, Chapter 6
Presentation
9.
Midterm Exam
10.
First Order Logic
AIMA, Chapter 8
11.
First Order Inference
AIMA, Chapter 9
12.
Uncertainty
AIMA, Chapter 13
13.
Bayesian Networks
AIMA, Chapter 14
14.
learning
AIMA, Chapter 18
15.
Learning part2
AIMA, Chapter 18
16.
Remaining Topics
17.
Project Presentation
Final Exam