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Introduction_to_Artificial intelligence_Lecture_Notes.pptx
- 1. Introduction to Artificial Intelligence CompleteLecture Notes for Undergraduate Students
- 2. Module 1: Foundationsof AI • Definition of AI (acting/thinking humanly, acting/thinking rationally) • History of AI: Turing, Dartmouth Conference, Expert Systems, Machine Learning, Deep Learning • Applications: Healthcare, Robotics, Finance, Natural Language Processing • Types of AI: Strong AI vs Weak AI
- 3. Intelligent Agents • Agent,Environment, Agent Function • Structure: Sensors, Actuators, Program • Rationality and Performance Measure • Types: Reflex, Model-based, Goal-based, Utility-based, Learning agents
- 4. Module 2: ProblemSolving and Search • Problem formulation: Initial state, Actions, Transition model, Goal test, Path cost • Example problems: Route finding, 8-puzzle, Missionaries and Cannibals
- 5. Uninformed Search Strategies •Breadth-First Search (BFS) • Depth-First Search (DFS) • Uniform-Cost Search • Properties: Completeness, Optimality, Time and Space Complexity
- 6. Informed Search Strategies •Heuristic Search • Greedy Best-First Search • A* Algorithm • Admissible Heuristics, Iterative Deepening A*
- 7. Adversarial Search • Gamesand Game Trees • Minimax Algorithm • Alpha-Beta Pruning • Evaluation Functions
- 8. Module 3: Knowledge Representationand Reasoning • Logic: Propositional and Predicate • Semantic Networks, Frames • Rule-Based Representation
- 9. Reasoning in AI •Deductive, Inductive, Abductive Reasoning • Forward and Backward Chaining • Resolution in Logic • Non-Monotonic Reasoning
- 10. Probabilistic Reasoning • Bayes’Theorem • Bayesian Networks • Hidden Markov Models (HMMs)
- 11. Module 4: MachineLearning Basics • Definition of Machine Learning • Learning Paradigms: Supervised, Unsupervised, Reinforcement Learning • Examples: Classification, Clustering, Decision- making
- 12. Statistical Learning • LinearRegression • Logistic Regression • Decision Trees • Naïve Bayes Classifier
- 13. Neural Networks (Basics) •Perceptron Model • Multi-layer Perceptrons (MLPs) • Backpropagation • The XOR Problem
- 14. Module 5: AdvancedTopics & Applications • Natural Language Processing (NLP) • Robotics: Perception, Planning, Control • Expert Systems: Knowledge Base, Inference Engine (e.g., MYCIN) • Ethics in AI: Bias, Privacy, Job Displacement, Accountability • Future of AI: AGI, Singularity, AI Safety