IT228 Artificial Intelligence
Artificial Intelligence notes
10 chapter notes, in syllabus order. Each starts with the key points.
Unit 1
AI Basics: Definitions, History, Agents, and ApplicationsUnit 1 of Artificial Intelligence introduces the core concepts of AI, including its definitions, historical evolution, types of AI, intelligent agents, and real-world applications. This note covers the foundational ideas that set the stage for advanced AI topics, with visuals, examples, and exam-focused insights.9 min readUnit 2
Intelligent Agents: Types, Environments & RationalityUnit 2 of Artificial Intelligence explores intelligent agents—autonomous entities that perceive and act in environments to achieve goals. This note covers agent types (reflex, model-based, goal-based, utility-based), environment characteristics (PEAS analysis), rationality, and real-world applications in apps like Path8 min readUnit 3
Problem Solving by Searching: Search Algorithms & State SpacesUnit 3 of Artificial Intelligence explores systematic methods to solve problems using search techniques, covering state spaces, search trees, uninformed (blind) and informed (heuristic) search strategies, and their applications in real-world scenarios like route planning and game playing.6 min readUnit 4
Informed Search & Game Playing: Heuristics, A, Minimax, Alpha-BetaUnit 4 of Artificial Intelligence explores how AI agents make smarter decisions by using domain knowledge (heuristics) to guide search, and how adversarial games like chess or Poker are solved using minimax with alpha-beta pruning. Covers A search, game trees, evaluation functions, and real-world applications in logist10 min readUnit 5
Knowledge Representation & Logic: FOL, Rules, Semantics & ProofsUnit 5 of Artificial Intelligence explores how to encode real-world knowledge using First-Order Logic (FOL), production rules, and semantic networks, then reason logically to derive new facts—critical for expert systems, databases, and AI agents.12 min readUnit 6
Bayes’ Theorem, Probabilistic Reasoning & Decision TreesUnit 6 of Artificial Intelligence covers probabilistic reasoning under uncertainty, Bayes’ theorem, conditional probability, decision trees, and their applications in real-world AI systems like medical diagnosis, spam filtering, and financial risk assessment.12 min readUnit 7
Machine Learning Basics: Models, Algorithms & ApplicationsUnit 7 of Artificial Intelligence explores supervised/unsupervised learning, regression/classification, model evaluation, and real-world ML pipelines, with visuals of decision trees, neural network layers, and loss functions.7 min readUnit 8
Neural Networks – Foundations, Training, and ApplicationsUnit 8 of Artificial Intelligence: introduces neural network concepts, architectures, training algorithms, and real‑world applications.8 min readUnit 9
Natural Language Processing – Key Concepts and ApplicationsUnit 9 of Artificial Intelligence: covers the fundamentals of NLP, from text preprocessing to advanced language models, and demonstrates how these techniques power modern applications.7 min readUnit 10
Expert Systems & AI Applications: Rules, Shells, and Real-World ImpactUnit 10 of Artificial Intelligence explores expert systems—how rule-based reasoning mimics human expertise, their architecture (knowledge base, inference engine), and real-world applications in medicine, finance, and industry. It also covers AI’s broader societal impact, ethical dilemmas, and emerging trends like AI in8 min read