What will you learn?
This course provides a foundation in the principles and problem-solving approaches of Artificial Intelligence, progressing from classical AI concepts to learning methods and contemporary AI technologies.
Core Topics
🧠 Foundations of Artificial Intelligence
AI concepts, history, advantages, limitations, contemporary applications, and AI development platforms.
🤖 Intelligent Agents
Agent design, task environments, and different types of intelligent agents.
🔍 Search & Problem Solving
Problem formulation, uninformed search, heuristic search, best-first search, greedy search, A* search, and local and global search strategies.
🎮 Adversarial Search
Game-playing environments, minimax reasoning, and alpha-beta pruning.
🧩 Knowledge Representation
Object–attribute–value representation, semantic networks, frames, and approaches for representing knowledge in intelligent systems.
⚙️ Logic & Automated Reasoning
Propositional logic, inference rules, forward and backward chaining, resolution, First-Order Logic, unification, and reasoning techniques.
📊 Reasoning Under Uncertainty
Probability, decision-making under uncertainty, Bayes’ rule, and Bayesian networks.
📚 Learning & Emerging AI
Fundamental learning concepts and algorithms, AI learning platforms, emerging technologies, and an introduction to areas such as quantum computing.
By the end of the course, students should be able to describe appropriate AI techniques for a given problem, discuss current AI developments and issues, and analyse the performance of AI algorithms in different problem scenarios.
- Teacher: Neha Gautam