Machine Learning

Unit 1

INTRODUCTION - Well defined learning problems, Designing a Learning System, Issues in Machine Learning; THE CONCEPT LEARNING TASK - General-to-specific ordering of hypotheses, Find-S, List then eliminate algorithm, Candidate elimination algorithm, Inductive bias

Unit 2

DECISION TREE LEARNING - Decision tree learning algorithm-Inductive bias- Issues in Decision tree learning; ARTIFICIAL NEURAL NETWORKS - Perceptrons, Gradient descent and the Delta rule, Adaline, Multilayer networks, Derivation of backpropagation rule Backpropagation Algorithm- Convergence, Generalization;

Unit 3

EVALUATING HYPOTHESES - Estimating Hypotheses Accuracy, Basics of sampling Theory, Comparing Learning Algorithms; BAYESIAN LEARNING - Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm;

Unit 4

COMPUTATIONAL LEARNING THEORY - Sample Complexity for Finite Hypothesis spaces, Sample Complexity for Infinite Hypothesis spaces, The Mistake Bound Model of Learning; INSTANCE-BASED LEARNING - k-Nearest Neighbor Learning, Locally Weighted Regression, Radial basis function networks, Case-based learning

Unit 5

GENETIC ALGORITHMS - an illustrative example, Hypothesis space search, Genetic Programming, Models of Evolution and Learning; Learning first order rules-sequential covering algorithms-General to specific beam search-FOIL; REINFORCEMENT LEARNING - The Learning Task, Q Learning.

REFERENCES:

1. Tom.M.Mitchell, Machine Learning, McGraw Hill International Edition

2. Ethern Alpaydin, Introduction to Machine Learning. Eastern Economy Edition, Prentice Hall of India, 2005.

3. Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer-Verlag.