Artificial Intelligence

Course Code: CS-3811

Course Structure: Lectures: 3/Labs: 0

 Credit Hours: 3  

Prerequisites:  CMP-2111(Discrete Structures) 

Artificial Intelligence Intro:

Artificial Intelligence is an approach to make a computer, a robot, or a product to think how smart human think. AI is a study of how human brain think, learn, decide and work, when it tries to solve problems. And finally, this study outputs intelligent software systems. The aim of AI is to improve computer functions which are related to human knowledge, for example, reasoning, learning, and problem-solving.

Learning outcomes:

Upon successful completion of this course, the student shall be able to:

 1) Demonstrate fundamental understanding of the history of artificial intelligence (AI) and its foundations.

2) Apply basic principles of AI in solutions that require problem solving, inference, perception, knowledge representation, and learning.

 3) Demonstrate awareness and a fundamental understanding of various applications of AI techniques in intelligent agents, expert systems, artificial neural networks and other machine learning models.

 4) Demonstrate proficiency developing applications in an 'AI language', expert system shell, or data mining tool.

5) Demonstrate proficiency in applying scientific method to models of machine learning. 6) Demonstrate an ability to share in discussions of AI, its current scope and limitations, and societal implications.

Course Syllabus: What is AI, Foundations of AI, History of AI. Weak AI, Strong AI.Intelligent Agents: Agents and Environments, The Nature of Environments, The Structure of Agents. Problem Solving by Searching.Breadth-First Search, Depth-First Search, Depth-limited Search, Iterative Deepening, Depth-first Search, Comparison of Uninformed Search Strategies. Informed Search and Exploration.Constraint Satisfaction Problems.Reasoning and Knowledge Representation.Inference in First-Order Logic.Introduction to Prolog Programming.Reasoning Systems for Categories.Reasoning with Uncertainty & Probabilistic Reasoning.Representing Knowledge in an Uncertain Domain.Learning from Observations.Knowledge in Learning. Statistical Learning, Neural Networks

Textbook(s):  Artificial Intelligence: A Modern Approach, by Russell and Norvig, Prentice Hall. 2ndEdition. ISBN-10: 0137903952 
 
Reference Material:  Artificial Intelligence: A Systems Approach by M. Tim Jones, Jones and Bartlett Publishers, Inc; 1stEdition (December 26, 2008). ISBN-10: 0763773379  Artificial Intelligence in the 21st Century by Stephen Lucci , Danny Kopec, Mercury Learning and Information (May 18, 2012). ISBN-10: 1936420236

Grading Scheme:

Mid Term:     30 Marks

Final Term:     50 Marks

Assignment & Quizzes:       20 Marks

Quiz: 10

Assignments: 10

BSCS 4th-Regular
Monday 08:30-09:30

Tuesday 08:30-09:30

BSCS 4th-Self

Tuesday 9:30-11:00

Wenesday 08:00-09:30