Course : Artificial Intelligence Instructor: Dr. Muhammad Adnan Hashmi Course Objective: Provide a concrete grasp of the fundamentals of various techniques and branches that currently constitute the field of Artificial Intelligence, e.g.,

1. Intelligent Agents 2. Search and Knowledge Representation 3. Autonomous planning 4. Machine learning 5. Robotics etc.

Moreover, train students into devising and implementing an AI Project.

Lecture No.

Topics Covered

1 Introduction to AI, History, State of the Art 2 Rational Agents, Environment Types 3 Agents Types 4 Problem Formulation, Problem Solving, Problem Solving Agents 5 Uninformed Search Strategies (Breadth First Search, Uniform Cost Search) 6 Uninformed Search Strategies (Depth First Search, Depth Limited Search) 7 Uninformed Search Strategies (Iterative Deepening Depth First Search,

Bidirectional Search) 8 Heuristic Based Search Strategies (Greedy Best First Search, A* Search) 9 Hill Climbing Search, Simulated Annealing 10 Adversarial Search (Min-Max Algorithm) 11 Alpha-Beta Pruning 12 Logic 13 First-Order Logic 14 Revision 15 Mid-Term Exam 16 Mid-Term Exam 17 Knowledge Representation 18 Knowledge Representation 19 Knowledge Representation 20 Planning- State Space Planning, Progression State Space Planning, Regression

State Space Planning 21 Partial Order Planning 22 Planning Graphs 23 Planning as Satisfiability 24 Learning, Supervised Learning 25 Supervised Learning 26 Un-Supervised Learning 27 K-NN Algorithm 28 Artificial Neural Networks 29 Artificial Neural Networks 30 Revision

Grading:

� Homework Assignments: 15% � Quizes: 5% � Mid-Term: 20% � Project: 20% � Final: 40%