My Notes

Unit I - Introduction to AIUnit I - Introduction to AIOverview This unit provides a foundational understanding of Artificial Intelligence (AI), tracing its history, evolution, and applications. It also distinguishes AI from Machine Learning (ML) and introduces key statistical tools and the concept of intelligent agents. Topics * Definitions of AI * Foundation and History of AI * Evolution of AI * Applications of AI * Classification of AI systems with respect to environment * Artificial Intelligence vs. Machine Learning * Statistical Analysis - C

Unit Il - Problem SolvingUnit Il - Problem SolvingOverview This unit delves into various problem-solving techniques in AI, focusing on heuristic search methods, constraint satisfaction problems (CSPs), and advanced search strategies beyond classical search algorithms. Topics * Heuristic Search Techniques * Generate-and-Test * Hill Climbing * A* Algorithm * Best-first Search * Problem Reduction * Constraint Satisfaction Problem (CSP) * Interference in CSPs * Backtracking Search for CSPs * Local Search for CSPs * Structure of

Unit III - Knowledge and ReasoningUnit III - Knowledge and ReasoningOverview This unit explores the mechanisms for building knowledge bases and reasoning in AI, including logic, theorem proving, planning, and handling uncertain knowledge through probabilistic methods. Topics * Building a Knowledge Base * Propositional Logic * First Order Logic * Situation Calculus * Theorem Proving in First Order Logic * Planning - Partial Order Planning * Uncertain Knowledge and Reasoning - Probabilities, Bayesian Networks * Probabilistic Reasoning Over Time * Hidden

Unit - IV LearningUnit - IV LearningOverview This unit covers various learning paradigms in AI, including supervised and unsupervised learning, decision trees, linear models, support vector machines (SVMs), ensemble learning, reinforcement learning, and artificial neural networks. Topics * Overview of Different Forms of Learning * Supervised Learning * Unsupervised Learning * Learning Decision Trees * Regression and Classification with Linear Models * Support Vector Machines (SVM) * Ensemble Learning * Reinforcement Learning *

Unit V - GameUnit V - GameOverview This unit examines AI in the context of games, focusing on search strategies, decision making under adversarial conditions, handling uncertainty, and the development of state-of-the-art game programs. Topics * Search under Adversarial Circumstances * Optimal Decision in Game * Minimax Algorithm * Alpha-Beta Pruning * Games with an Element of Chance * Imperfect Real Time Decision * Stochastic Games * Partially Observable Games * State-of-Art Game Program * Alternative Approaches

Unit VI - Expert SystemsUnit VI - Expert SystemsOverview This unit introduces expert systems, their inference mechanisms, tools and languages used for their development, explanation facilities, and knowledge acquisition processes. It also explores applications in natural language processing and computer vision. Topics * Introduction to Expert Systems * Inference - Forward Chaining, Backward Chaining * Languages and Tools * Explanation Facilities * Knowledge Acquisition * Applications * Natural Language Processing * Sentiment Analysis

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Link to original note: Honours - Artificial Intelligences and Machine LearningHonours - Artificial Intelligences and Machine LearningCourse Objectives Course Outcomes Course Contents AI-Notes Learning Resources