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ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Semester :  VII

Course Code : 18CS71

CIE Marks : 40                       SEE Marks : 60

 Module – 1

Printed Notes

 Module – 2

Printed Notes

 Module – 3

Printed Notes

 Module – 4

Printed Notes

 Module – 5

Printed Notes

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
18CS71

SYLLABUS

Module-1

What is artificial intelligence?, Problems, problem spaces and search, Heuristic search techniques
Textbook 1: Chapter 1, 2 and 3 

Module-2

Knowledge representation issues, Predicate logic, Representation knowledge using rules. Concept Learning: Concept learning task, Concept learning as search, Find-S algorithm, Candidate Elimination Algorithm, Inductive bias of Candidate Elimination Algorithm.
Texbook 1: Chapter 4, 5 and 6 Texbook2: Chapter 2 (2.1-2.5, 2.7)

Module-3

Decision Tree Learning: Introduction, Decision tree representation, Appropriate problems, ID3 algorithm. Artificial Neural Network: Introduction, NN representation, Appropriate problems, Perceptrons, Backpropagation algorithm.
Texbook2: Chapter 3 (3.1-3.4), Chapter 4 (4.1-4.5) 

Module-4

Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting, MDL principle, Bates optimal classifier, Gibbs algorithm, Navie Bayes classifier, BBN, EM Algorithm
Texbook2: Chapter 6

Module-5

Instance-Base Learning: Introduction, k-Nearest Neighbour Learning, Locally weighted regression, Radial basis function, Case-Based reasoning. Reinforcement Learning: Introduction, The learning task, Q-Learning.
Textbook 1: Chapter 8 (8.1-8.5), Chapter 13 (13.1 – 13.3)