Artificial Intelligence Syllabus

Prerequisite:

• Students should have basic knowledge of Python programming, basic mathematics, and general computer skills like file handling, using OS. Familiarity with common technologies is also helpful.

Course Objective:

• To understand the fundamentals of AI, its real-world applications, and basic problem-solving techniques using search algorithms. They will learn about knowledge representation, expert systems, and reasoning methods

Unit-01 Introduction to AI (24%)

• Definition and Evolution of AI and real-life examples
• Difference between AI, Machine Learning, and Deep Learning, Types of AI
• Applications of AI: Healthcare, Finance, Entertainment, Education
• Turing Test: Concept of Turing Test
• AI Challenges: Challenges in AI
• AI Problems: Simple real-world AI problems
• Basic AI Techniques: Rule-based systems
• Simple decision-making using conditions

Unit-02 Problem Solving in AI (29%)

• Defining AI Problems: Problem space, State-space representation
• Search Strategies: Uninformed and Informed Search
• Search Algorithms: Breadth-First Search, Depth-First Search
• Hill Climbing Algorithm
• Heuristic Search: definition of Heuristic
• An educated guess to speed up the search, e.g.: Google Maps suggesting faster routes

Unit-03 Knowledge Representation (28%)

• Introduction, Storing information for AI systems
• Types of Knowledge Representation
• Declarative Knowledge: Facts and rules
• Procedural Knowledge: Step-by-step procedures
• Rules and Expert Systems: definition of Expert System
• Characteristics of Expert Systems
• Advantages and Limitations
• Forward Reasoning and Backward Reasoning

Unit-04 AI Applications (20%)

• AI-Based Planning and Scheduling
• Natural Language Processing
• Neural Networks
• Advanced AI Techniques: Expert Systems, Genetic Algorithms, Future Trends
• Game Playing and Ethics: AI in Games, How AI is used in gaming
• Minimax Algorithm
• Bias in AI
• Privacy Concerns
• Transparency and Fairness

---