Artificial Intelligence- CS-3811

Artificial Intelligence- CS-3811

Introduction:

This course will introduce the basic principles in artificial intelligence. It will cover simple representation schemes, problem solving paradigms, constraint propagation, and search strategies. Areas of application such as knowledge representation, natural language processing, expert systems, vision and robotics will be explored. The Prolog programming language will also be introduced.

Course Prerequisites
- CMP-2111(Discrete Structures)
Learning outcomes

After completing this course, you should be able to:

what is artificial intelligence and its importance.
Discover Different application area of artificial Intelligence.
Research Area in machine learning.

Text Book
- Artificial Intelligence: A Modern Approach ; (Third edition) by Stuart Russell and Peter Norvig, Published by Pearson

Reference Material

Virtual University Of Pakistan , Course helping material Video lectures https://ocw.vu.edu.pk/Videos.aspx?cat=Computer+Science%2fInformation+Technology+&course=CS607
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 Booch, James Rumbaugh and Ivar Jacobson, Addison-Wesley Professional; (2005). ISBN-10: 0321267974.

Description of system Evaluation

Assessment	Marks	Assessment	Marks
Mid Term	30	Final Term	50
Project	7	Quizzes	7
Presentation	3	Assignment	3

Details lesson plans

Lectures	Topic	Reading
Week 1	Introduction: What is AI, Foundations of AI, and History of AI. Intelligent Agents: Agents and Environments, The Nature of Environments, The Structure of Agents.	Chapter 1,2
Week 2	Problem Solving by Searching: Problem Solving Agents, Searching for Solutions, Uninformed Search Strategies.	Chapter 1,2
Week 3	Breadth-First Search, Depth-First Search, Depth-limited Search, Iterative Deepening, Depth-first Search, Comparison of Uninformed Search Strategies.	Chapter 3
Week 4	Informed Search and Exploration: Informed (Heuristic) Search Strategies: Greedy Bestfirst Search, A* Search, Heuristic Functions, Local Search Algorithms and Optimization Problems.	Chapter 4
Week 5	Adversarial Search: Games, Minimax Algorithm, Alpha-Beta Pruning.	Chapter 5
Week 6	Reasoning and Knowledge Representation: Introductions to Reasoning and Knowledge Representation, Propositional Logic, First Order Logic: Syntax and Semantics of FirstOrder Logic,	Chapter 6
Week 7	Inference in First-Order Logic: Inference rules for quantifiers, A first-order inference rule,	Chapter 9
Week 8	Midterm Exams
Week 9	Introduction to Prolog Programming
Week 10	Reasoning Systems for Categories, Semantic Nets and Description logics, Reasoning with Default Information	Chapter 10
Week 11	Reasoning with Uncertainty & Probabilistic Reasoning : Acting Under Uncertainty, Bayes' Rule and Its Use,	Chapter 13
Week 12	Representing Knowledge in an Uncertain Domain,	Chapter 14
Week 13	Learning from Observations: Forms of Learning , Inductive Learning,, Learning Decision Trees.	Chapter 18
Week 14	Knowledge in Learning,	Chapter 19
Week 15	Statistical Learning, Neural Networks	Chapter 20
Week 16	Final Exams

Key dates and Time

PPP BSSE 6th - A
- Monday (2:0pm-3:30pm)
- Wednesday (5:0pm-6:30pm)

PPP BSIT 6th - B
- Monday (03:30 pm- 05:00pm)
- Tuesday (05:0 pm-06:30 pm)
PPP BSIT 6th – A
- Tuesday (3:30 pm-5:0 pm)
- Wednesday (02:0 pm - 3:30pm)
PPP BSCS 6th - E
- Tuesday (2:0pm-3:30pm)
- Wednesday (3:30-5:0pm)