IT228 Artificial Intelligence
Artificial Intelligence notes
10 chapter notes, in syllabus order. Each starts with the key points.
Unit 1
AI Basics: Definitions, History, Applications & EthicsUnit 1 of Artificial Intelligence introduces core concepts like AI definitions, its history, types (narrow vs. general), applications in real-world systems, and ethical considerations—essential for understanding how AI transforms industries and daily life.20 min readUnit 2
Intelligent Agents: Types, Environments & RationalityUnit 2 of Artificial Intelligence explores intelligent agents—autonomous entities that perceive and act in environments—covering agent types (reflex, model-based, goal-based), environments (accessible vs. unobservable, deterministic vs. stochastic), rationality, and performance measures.10 min readUnit 3
Problem Solving by Searching: Algorithms, Trees & Real-World ApplicationsUnit 3 of Artificial Intelligence explores systematic problem-solving techniques using search algorithms (BFS, DFS, UCS), state spaces, game trees, and heuristic methods, with real-world ties to apps like Pathao (route optimization) and Ncell (network pathfinding).12 min readUnit 4
Informed Search & Game Trees: Heuristics, A, MinimaxUnit 4 of Artificial Intelligence covers heuristic search algorithms (Greedy, A, Uniform Cost), game theory (Minimax, Alpha-Beta pruning), and their applications in decision-making, pathfinding, and competitive AI—with real-world examples from eSewa, Daraz, and Ncell.12 min readUnit 5
Knowledge Representation & Logic: Facts, Rules & ReasoningUnit 5 of Artificial Intelligence explores how to encode real-world knowledge as data structures (propositional, predicate, frames, semantic networks) and use logical rules (inference, resolution, forward/backward chaining) to derive new facts—foundations for expert systems and AI reasoning.14 min readUnit 6
Probability, Bayes’ Theorem, Uncertainty in AIUnit 6 of Artificial Intelligence covers probabilistic reasoning, Bayes’ theorem, decision trees, and handling uncertainty in AI systems—key for diagnosing diseases, spam filtering, and autonomous systems.5 min readUnit 7
Machine Learning Basics: Models, Algorithms & ApplicationsUnit 7 of Artificial Intelligence explores foundational concepts of machine learning, including supervised/unsupervised learning, model evaluation, bias-variance tradeoff, and real-world applications in Nepalese and global tech ecosystems. This note covers definitions, algorithms, worked examples (e.g., loan approval, 7 min readUnit 8
Neural Networks: Layers, Learning & ApplicationsUnit 8 of Artificial Intelligence explores neural networks—how they mimic biological neurons, process data through layers, and learn via backpropagation, with real-world examples from Nepalese tech (eSewa fraud detection, Ncell chatbots) and global AI (Google Translate, YouTube recommendations).9 min readUnit 9
NLP: Text Processing, Chatbots, and Language ModelsUnit 9 of Artificial Intelligence explores Natural Language Processing (NLP), covering text preprocessing, tokenization, parsing, semantic analysis, and real-world applications like chatbots and translation systems. Learn how machines understand human language, from rule-based systems to deep learning models like trans17 min readUnit 10
Expert Systems & AI Applications: Rules, Inference, and Real-World ImpactUnit 10 of Artificial Intelligence explores expert systems—how rule-based reasoning mimics human expertise, their architecture (knowledge base, inference engine, user interface), and real-world applications in medicine, finance, and more. Covers forward/backward chaining, certainty factors, and AI’s societal impact, wi7 min read