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)