Machine Learning–Types of Machine Learning –Machine Learning process- preliminaries, testing Machine Learning algorithms, turning data into Probabilities, and Statistics for Machine Learning Probability theory – Probability Distributions – Decision Theory.
Linear Models for Regression – Linear Models for Classification- Discriminant Functions, Probabilistic Generative Models, Probabilistic Discriminative Models – Decision Tree Learning – Bayesian Learning, Naïve Bayes – Ensemble Methods, Bagging, Boosting
Neural Network Representation – Problems – Perceptron – Multilayer Networks and Back Propagation Algorithms - Convolutional neural networks - Recurrent neural networks – Create and deploy neural networks using Tensor Flow and Keras
Clustering- K-means – EM Algorithm- Mixtures of Gaussians –Dimensionality Reduction, Linear Discriminant Analysis, Factor Analysis, Principal Components Analysis, Independent Components Analysis
Introduction - Single State Case - Elements of Reinforcement Learning – Model Based Learning - Temporal Difference Learning – Q Learning Algorithm – Generalization - Partially Observable States.
Reference Book:
1. AlpaydinEthem, “Introduction to Machine Learning”, MIT Press, Second Edition, 2010 2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An Introduction to Statistical Learning: with Applications in R”, Springer; First Edition 2013 3. P. Flach, ―Machine Learning: The art and science of algorithms that make sense of data,Cambridge University Press, 2012.
Text Book:
1. Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (India) Private Limited, 2013. 2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer; Second Edition, 2009
