Introduction to Artificial Intelligence

This course offers a comprehensive introduction to the field of Artificial Intelligence, tailored for beginners and enthusiasts eager to explore how intelligent systems are designed and function. Structured across five focused sections, the course blends foundational theory with practical insights, covering essential topics such as AI techniques, search algorithms, intelligent agents, knowledge representation, and expert systems. In Section 1: Introduction to AI, you'll explore the fundamentals of AI including various techniques, models, and problem-solving approaches. This section covers how AI formulates problems, identifies problem types, and navigates the problem space using classic examples like the Tic-Tac-Toe game, the Missionaries and Cannibals puzzle, and the Travelling Salesman Problem.Section 2: Basic Introduction to Data Structures and Search Algorithms focuses on the core data structures such as stacks, queues, trees, and graphs. You'll learn how AI uses these structures in problem-solving, along with search techniques like Breadth-First Search, Depth-First Search, and informed strategies including A* and Best-First Search. It also covers control strategies and agent-based search models.In Section 3: Adversarial Search Problems and Intelligent Agents, you'll study how AI handles competitive scenarios using game theory. Key concepts include the Minimax algorithm, Alpha-Beta Pruning, and Constraint Satisfaction Problems (CSPs). Additionally, this section explores intelligent agents—how they reason, make decisions, and operate in various environments.Section 4: Knowledge Representation dives into how AI systems store, structure, and infer knowledge. Topics include propositional and predicate logic, inference mechanisms, semantic networks, unification algorithms, and reasoning with uncertainty. You'll also learn about different ways to represent knowledge using rules, frames, and logic.Lastly, Section 5: Planning and Expert