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, and vision will be explored.

COURSE CODE: CS-3811

CREDIT HOURS: 03

PREREQUISITES: CMP-2111(Discrete Structures)

COURSE LEARNING OUTCOMES

At the end of the course the students will be able to:

• Differentitae between AI problems and Non-AI problems.
• Describe the general understanding of the principles and concepts governing the functions of Artificial Intelligence.
• Students will be able to define and describe problem solving approaches related to different deterministic and non deterministic problems.

TEXT BOOK: Artificial Intelligence: A Modern Approach, by Russell and Norvig, Prentice Hall. 2ndEdition. ISBN-10: 0137903952

COURSE CONTENTS

• Introduction: What is AI, Foundationsof AI, History of AI. Intelligent Agents: Agents and Environments, The Nature of Environments, The Structure of Agents.
• Problem Solving by Searching: Problem Solving Agents, Searching for Solutions, Uninformed Search Strategies.
• Breadth-First Search, Depth-First Search, Iterative Deepening Search, Comparison of Uninformed Search Strategies.
• Informed Search and Exploration: Informed (Heuristic) Search Strategies: Greedy Best-first Search, A* Search, Heuristic Functions, Local Search Algorithms and Optimization Problems.
• Constraint Satisfaction Problems: Backtracking Search for CSPs, Local Search for CSPs. Minimax Algorithm, Alpha-Beta Pruning.
• Reasoning and Knowledge Representation: Introductions to Reasoning and Knowledge Representation, Propositional Logic, First Order Logic: Syntax and Semantics of First-Order Logic, Knowledge Engineering in First-Order Logic.
• Inference in First-Order Logic: Inference rules for quantifiers, A first-order inference rule, Wumpus World Problem.
• Machine Learning Introduction, Classification: Decision Tree, Bayesian Classification, Regression: Uni-variate Linear regression, Multi-variate Linear regression.
• Neural Networks, Single Layer, Multi Layer perceptron, BackPropagation.
• Evolutionary Algorithms: Genetic Algorithm, Simulated Annealing.

COURSE ASSESSMENT:

• Final Term Exam: 50 Marks
• Mid Term Exam: 30 Marks
• Sessional: 20 Marks
  • Quiz: 03 Marks
  • Assignment: 04 Marks
  • Project: 10 Marks
  • Class Participation: 3 marks

CLASS TIMING:

• BSCS 6th A (Monday: 02:00 - 03:30 PM) (Thursday: 12:30 - 02:00 PM)
• BSCS 6th B (Monday: 12:30 - 02:00 PM) (Tuesday: 12:30 - 02:00 PM)