Artificial Intelligence

Understand the fundamentals of Artificial Intelligence, intelligent agents, search techniques, and how AI approaches real-world problem-solving through rational and goal-based systems.

Key Topics:

• Definition and Meaning of Artificial Intelligence
• Scope and Importance of AI in Modern Technology
• General Issues and Overview of AI
• AI Techniques – Search, Knowledge, and Learning Approaches
• AI Problems and Their Characteristics
• Intelligent Agents – Definition and Role in AI
• Types of Agents – Rational, Simple Reflex, Model-Based, Goal-Based, and Utility-Based
• Agent’s Environment – Types of Environments (Deterministic, Static, Dynamic, etc.)
• Problem Solving as State Space Search – Definition and Steps
• Production System – Components, Search Space, and Control Strategies

Learn different search strategies for problem-solving. Explore uninformed and informed search techniques such as BFS, DFS, Hill Climbing, A*, AO*, and CSPs.

Key Topics:

• Introduction to Search Algorithms and Their Role in AI
• Types of Search Algorithms – Uninformed and Informed Search
• Uninformed (Blind) Search: Breadth-First Search (Steps & Example)
• Uninformed (Blind) Search: Depth-First Search (Steps & Example)
• Informed (Heuristic) Search – Introduction and Uses
• Hill Climbing Search and Its Variations (Simple, Steepest-Ascent, Random)
• Best-First Search Algorithm
• A* (A-Star) Search Algorithm
• AO* Search and Branch & Bound Techniques
• Problem Reduction and Constraint Satisfaction Problems (CSPs)

Study how knowledge is represented, processed, and reasoned in AI systems using logic, semantic networks, frames, and uncertainty handling methods like Bayes’ theorem.

Key Topics:

• Knowledge Representation – Definition and Importance
• Predicate Logic – Unification and Modus Ponens
• Declarative and Procedural Knowledge Representation
• Rule-Based Systems – Concept and Application
• Structured Representation – Semantic Networks
• Frames – Slots, Defaults, and Exceptions
• Conceptual Dependency – Meaning Representation
• Handling Incomplete and Inconsistent Knowledge – Truth Maintenance Systems (TMS)
• Reasoning Techniques – Deductive, Inductive, and Non-Monotonic Reasoning
• Concept of Uncertainty and Bayes’ Theorem

Explore core learning paradigms such as supervised, unsupervised, and reinforcement learning, and understand algorithms like Decision Trees, Ensemble Methods, and Inductive Logic Programming.

Key Topics:

• Forms of Learning – Supervised, Unsupervised, Reinforcement
• Learning from Observations and Examples
• Inductive Learning – Concept and Applications
• Decision Tree Learning (ID3, C4.5)
• Ensemble Learning – Bagging, Boosting, Random Forests
• Knowledge in Learning – Logical Formulation of Learning
• Explanation-Based Learning (EBL)
• Inductive Logic Programming (ILP)
• Statistical Learning Methods – Regression, Probability Models
• Instance-Based Learning – k-Nearest Neighbor and Case-Based Reasoning