Syllabus

Introduction to Artificial Intelligence – Definition of AI; History and evolution of AI; Applications of AI in various domains. Problem Solving and Search Algorithms — Problem formulation, state space search, uninformed strategies (BFS, DFS), informed strategies (A*, Greedy best-first), heuristic functions, adversarial search (Minimax, Alpha-beta pruning).

Information Retrieval –Overview of Information Retrieval systems; Role of search algorithms in information retrieval; Indexing and document representation; Evaluation metrics for information retrieval; Applications in search engines and databases. 

Intelligent Agents – Definition and characteristics of intelligent agents; Types of intelligent agents: simple reflex, model-based, goal-based, utility-based; Agent architecture and functionality; Learning methods for intelligent agents; Case studies of intelligent agents in real-world applications. Multi-Agent Systems -Communication among agents, cooperation and coordination, distributed decision-making, game-theoretic foundations, applications in collaborative environments and traffic systems.

Natural Language Processing (NLP) Basics – Introduction to NLP and its significance; Text preprocessing techniques; Tokenization, stemming, and lemmatization; Basic syntactic parsing and part-of-speech tagging; Introduction to sentiment analysis and language models; Applications of NLP in chatbots and virtual assistants. 

Robotics Basics – Introduction to Robotics and its relation to AI; Components of a robotic system (sensors, actuators, controllers); Basic robot kinematics and motion planning; Introduction to robotic perception; Case studies of AI applications in robotics (e.g., industrial robots, autonomous vehicles).

Ethics in AI – Importance of ethical considerations in AI; Fairness, accountability, and transparency; Bias in AI systems; Privacy and data protection; Responsible AI and regulatory frameworks; Case studies highlighting ethical challenges in AI.