Artificial Intelligence Course Material.pdf

41 Lessons in 13 modules of Artificial Intelligence ebook, I think these materials are taken from AI web course held by an Indian University. Pictures and words work together to explain as concise as possible everything about Artificial Intelligence, you can find logic, fuzzy, agent, single agent search and more…

This ebook is available FREE at National Programme on Technology Enhanced Learning Indian Institute of Technology Madras, India website, we merely collect the information, we are neither affiliated with the author(s), the website and any brand nor responsible for its content and change of content. (Read our disclaimer here or here before you download the document from the website written above by clicking the below link).

Table of Content [along with download links]:

• Module 1 Introduction
  • Lesson 1 Introduction to AI
    • 1.1.1 Definition of AI
    • 1.1.2 Typical AI problems
    • 1.1.3 Practical Impact of AI
    • 1.1.4 Approaches to AI
    • 1.1.5 Limits of AI Today
    • 1.2 AI History
  • Lesson 2 Introduction to Agent
    • 1.3.2 Agent Environment
    • 1.3.3 Agent architectures
• Module 2 Problem Solving using Search-(Single agent search)
  • Lesson 3 Introduction to State Space Search
    • 2.2 State space search
    • 2.3 Examples
    • Explicit vs Implicit state space
  • Lesson 4 Uninformed Search
    • 2.4 Search
  • Lesson 5 Informed Search Strategies-I
    • 3.1 Introduction
    • 3.2 Best First Search
  • Lesson 6 Informed Search Strategies-II
    • 3.3 Iterative-Deepening A*
    • 3.4 Other Memory limited heuristic search
    • 3.5 Local Search
• Module 3 Problem Solving using Search-(Two agent)
  • Lesson 7 Adversarial Search
  • Lesson 8 Two agent games : alpha beta pruning
    • 3.5 Alpha-Beta Pruning
• Module 4 Constraint satisfaction problems
  • Lesson 9 Constraint satisfaction problems - I
    • 4.2 Constraint Satisfaction Problems
    • 4.3 Representation of CSP
    • 4.4 Solving CSPs
  • Lesson 10 Constraint satisfaction problems - II
    • 4.5 Variable and Value Ordering
    • 4.6 Heuristic Search in CSP
• Module 5 Knowledge Representation and Logic (Propositional Logic)
  • Lesson 11 Propositional Logic
    • 5.2 Knowledge Representation and Reasoning
    • 5.3 Propositional Logic
    • 5.4 Propositional Logic Inference
  • Lesson 12 Propositional Logic inference rules
    • 5.5 Rules of Inference
    • 5.6 Using Inference Rules to Prove a Query/Goal/Theorem
    • 5.7 Soundness and Completeness
• Module 6 Knowledge Representation and Logic (First Order Logic)
  • Lesson 13 First Order Logic - I
    • 6.2 First Order Logic
    • 6.2.3 Unification
    • 6.2.4 Semantics
  • Lesson 14 First Order Logic - II
    • 6.2.5 Herbrand Universe
    • 6.2.6 Deduction
    • 6.2.7 Soundness, Completeness, Consistency, Satisfiability
  • Lesson 15 Inference in FOL - I
    • 6.2.8 Resolution
    • 6.2.8.2 Resolution in First Order Logic
  • Lesson 16 Inference in FOL - II
    • 6.2.9 Proof as Search
    • 6.2.10 Some Proof Strategies
    • 6.2.11 Non-Monotonic Reasoning
• Module 7 Knowledge Representation and Logic (Rule based Systems)
  • Lesson 17 Rule based Systems - I
    • 7.2 Rule Based Systems [ 7.2.1 Horn Clause Logic ~ 7.2.2 Backward Chaining ~ 7.2.3 Pure Prolog ~ 7.2.4 Forward chaining ]
  • Lesson 18 Rule based Systems - II
    • 7.2.5 Programs in PROLOG
    • 7.2.6 Expert Systems
• Module 8 Other representation formalisms
  • Lesson 19 Semantic nets
    • 8. 2 Knowledge Representation Formalisms
    • 8.3 Semantic Networks
  • Lesson 20 Frames - I [DISTINCTION BETWEN SETS AND INSTANCES]
  • Lesson 21 Frames II
    • Slots as Objects [ Interpreting frames ~ Access Paths ]
• Module 9 Planning
  • Lesson 22 Logic based planning
    • 9. 1 Introduction to Planning
    • 9.2 Logic Based Planning
  • Lesson 23 Planning systems
    • 9.3 Planning Systems [ 9.3.1 Representation of States and Goals ~ 9.3.2 Representation of Action ]
  • Lesson 24 Planning algorithm - I
    • 9.4 Planning as Search
  • Lesson 25 Planning algorithm - II
    • 9.4.5 Partial-Order Planning
    • 9.5 Plan-Space Planning Algorithms
• Module 10 Reasoning with Uncertainty - Probabilistic reasoning
  • Lesson 26 Reasoning with Uncertain information
    • 10. 2 Probabilistic Reasoning
    • 10.3 Review of Probability Theory
  • Lesson 27 Probabilistic Inference
    • 10.4 Probabilistic Inference Rules
  • Lesson 28 Bayes Networks
    • 10.5 Bayesian Networks
    • 10.5.2 Semantics of Bayesian Networks
    • 10.5.4 Learning of Bayesian Network Parameters
  • Lesson 29 A Basic Idea of Inferencing with Bayes Networks
    • 10.5.5 Inferencing in Bayesian Networks
    • 10.5.6 Approximate Inferencing in Bayesian Networks
• Module 11 Reasoning with uncertainty-Fuzzy Reasoning
  • Lesson 30 Other Paradigms of Uncertain Reasoning
    • 11.2 Reasoning with Uncertainty [ 11.2.1 THE PROBLEM: REAL-WORLD VAGUENESS ~ 11.2.2 HISTORIC FUZZINESS ]
  • Lesson 31 Fuzzy Set Representation
    • 11.3 Fuzzy Sets: BASIC CONCEPTS [ 11.3.1 HEDGES ]
  • Lesson 32 Fuzzy Reasoning - Continued
    • 11.4 Fuzzy Inferencing
    • 11.5 APPLICATIONS
• Module 12 Machine Learning
  • Lesson 33 Learning : Introduction
    • 12.1 Introduction to Learning [ 12.1.1 Taxonomy of Learning Systems ~ 12.1.2 Mathematical formulation of the inductive learning problem ]
  • Lesson 34 Learning From Observations
    • 12.2 Concept Learning
  • Lesson 35 Rule Induction and Decision Tree - I
    • 12.3 Decision Trees
  • Lesson 36 Rule Induction and Decision Tree - II
    • Splitting Functions
    • 12.3.4 Decision Tree Pruning
  • Lesson 37 Learning and Neural Networks - I
    • 12.4 Neural Networks [ 12.4.1 Biological Neural Networks ~ 12.4.2 Artificial Neural Networks ]
  • Lesson 38 Neural Networks - II
    • 12.4.3 Perceptron [12.4.3.1 Perceptron Learning ~ The Perceptron Rule ~ The Delta Rule ]
  • Lesson 39 Neural Networks - III
    • 12.4.4 Multi-Layer Perceptrons [ 12.4.4.1 Back-Propagation Algorithm ~ Forward Propagation ~ Backward Propagation ]
• Module 13 Natural Language Processing
  • Lesson 40 Issues in NLP
    • 13.1 Natural Language Processing [ 13.1.1 Ambiguity ~ 13.1.2 Models to represent Linguistic Knowledge ~ 13.1.3 Algorithms to Manipulate Linguistic Knowledge ]
    • 13.2 Natural Language Understanding
  • Lesson 41 Parsing
    • 13.3 Natural Language Generation
    • 13.4 Steps in Language Understanding and Generation
    • 13.5 Knowledge Representation for NLP
    • 13.6 Discourse
    • 13.7 Applications of Natural Language Processing
    • 13.8 Machine Translation

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