Introduction to Artificial Intelligence 2025

Problem Solving, Planning, Searching, Knowledge Base Agents, Intelligent Agents, Planning and Expert Systems

What you’ll learn
Formulate a problem as a state space search method and its solution using various AI techniques
Apply appropriate searching techniques to solve a real-world problem
Develop various game playing strategies to solve real world adversarial search problems
Represent various knowledge representation techniques to solve complex AI problems
Design an expert system to implement advance techniques in Artificial Intelligence

Requirements
Nil

Description
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 Systems introduces planning techniques and machine learning fundamentals. You’ll explore planning problems, means-ends analysis, and the Blocks World scenario. This section also presents the basics of expert systems, including their architecture and how they simulate human decision-making.This course is ideal for students, aspiring AI developers, and anyone curious about how machines can think, learn, and act intelligently. By the end, you’ll have a strong foundation to explore more advanced AI topics or pursue practical projects in the field.

Who this course is for
Beginners and Intermediate