AI
Module 1 Introduction
o 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
o Lesson 2 Introduction to Agent
1.3.2 Agent Environment
1.3.3 Agent architectures
Module 2 Problem Solving using Search-(Single agent search)
o Lesson 3 Introduction to State Space Search
2.2 State space search
2.3 Examples
Explicit vs Implicit state space
o Lesson 4 Uninformed Search
2.4 Search
o Lesson 5 Informed Search Strategies-I
3.1 Introduction
3.2 Best First Search
o 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)
o Lesson 7 Adversarial Search
o Lesson 8 Two agent games : alpha beta pruning
3.5 Alpha-Beta Pruning
Module 4 Constraint satisfaction problems
o Lesson 9 Constraint satisfaction problems - I
4.2 Constraint Satisfaction Problems
4.3 Representation of CSP
4.4 Solving CSPs
o 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)
o Lesson 11 Propositional Logic
5.2 Knowledge Representation and Reasoning
5.3 Propositional Logic
5.4 Propositional Logic Inference
o 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)
o Lesson 13 First Order Logic - I
6.2 First Order Logic
6.2.3 Unification
6.2.4 Semantics
o Lesson 14 First Order Logic - II
6.2.5 Herbrand Universe
6.2.6 Deduction
6.2.7 Soundness, Completeness, Consistency, Satisfiability
o Lesson 15 Inference in FOL - I
6.2.8 Resolution
6.2.8.2 Resolution in First Order Logic
o 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)
o 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 ]
o Lesson 18 Rule based Systems - II
7.2.5 Programs in PROLOG
7.2.6 Expert Systems
Module 8 Other representation formalisms
o Lesson 19 Semantic nets
8. 2 Knowledge Representation Formalisms
8.3 Semantic Networks
o Lesson 20 Frames - I [DISTINCTION BETWEN SETS AND INSTANCES]
o Lesson 21 Frames II
Slots as Objects [ Interpreting frames ~ Access Paths ]
Module 9 Planning
o Lesson 22 Logic based planning
9. 1 Introduction to Planning
9.2 Logic Based Planning
o Lesson 23 Planning systems
9.3 Planning Systems [ 9.3.1 Representation of States and Goals ~ 9.3.2
Representation of Action ]
o Lesson 24 Planning algorithm - I
9.4 Planning as Search
o Lesson 25 Planning algorithm - II
9.4.5 Partial-Order Planning
9.5 Plan-Space Planning Algorithms
Module 10 Reasoning with Uncertainty - Probabilistic reasoning
o Lesson 26 Reasoning with Uncertain information
10. 2 Probabilistic Reasoning
10.3 Review of Probability Theory
o Lesson 27 Probabilistic Inference
10.4 Probabilistic Inference Rules
o Lesson 28 Bayes Networks
10.5 Bayesian Networks
10.5.2 Semantics of Bayesian Networks
10.5.4 Learning of Bayesian Network Parameters
o 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
o 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 ]
o Lesson 31 Fuzzy Set Representation
11.3 Fuzzy Sets: BASIC CONCEPTS [ 11.3.1 HEDGES ]
o Lesson 32 Fuzzy Reasoning - Continued
11.4 Fuzzy Inferencing
11.5 APPLICATIONS
Module 12 Machine Learning
o 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 ]
o Lesson 34 Learning From Observations
12.2 Concept Learning
o Lesson 35 Rule Induction and Decision Tree - I
12.3 Decision Trees
o Lesson 36 Rule Induction and Decision Tree - II
Splitting Functions
12.3.4 Decision Tree Pruning
o Lesson 37 Learning and Neural Networks - I
12.4 Neural Networks [ 12.4.1 Biological Neural Networks ~ 12.4.2 Artificial
Neural Networks ]
o Lesson 38 Neural Networks - II
12.4.3 Perceptron [12.4.3.1 Perceptron Learning ~ The Perceptron Rule ~ The
Delta Rule ]
o 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
o 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
o 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