Introduction

Aspects of machine learning, Supervised, Unsupervised, Semi-supervised and Reinforcement learning, Evaluation of hypothesis, Practical applications of machine learning.

Artificial Neural Networks

Neurons and biological motivation, Perceptron and solving Boolean functions, Feed forward and recurrent networks, Single layer and multilayer networks, Back-propagation training method, Radial basis function networks, Associative memory, Ensemble methods.

Support Vector Machines

Linear maximal margin classifier, Linear soft margin classifier; Nonlinear classifier.

Decision Trees

Recursive induction, Splitting attribute selection, Entropy and information Gain, Overfitting and pruning, ID3 and C4.5 algorithms.

Genetic Algorithms

Motivation from natural evolution, Genetic operators, Fitness function, Genetic algorithms for optimization.

Swarm Intelligence

Features of natural swarms, Swarm based methods for

optimization

Ant colony optimization, Particle swarm optimization, Bee colony optimization.

Clustering and Unsupervised Learning

Learning from unclassified data, Clustering, Hierarchical agglomerative clustering, K-means partitional clustering.

Dimensionality Reduction

Curse of the dimensionality, Empty space phenomenon, Linear and nonlinear techniques for dimensionality reduction.