Machine Learning – Types of Machine Learning – Supervised Learning – Unsupervised Learning – Basic Concepts in Machine Learning – Machine Learning Process – Weight Space – Testing Machine Learning Algorithms – A Brief Review of Probability Theory – Turning Data into Probabilities – The Bias-Variance Tradeoff.
Linear Models for Regression – Linear Basis Function Models – The Bias-Variance Decomposition – Bayesian Linear Regression – Common Regression Algorithms – Simple Linear Regression – Multiple Linear Regression – Linear Models for Classification – Discriminate Functions – Probabilistic Generative Models – Probabilistic Discriminative Models – Laplace Approximation – Bayesian Logistic Regression – Common Classification Algorithms – k-Nearest Neighbors – Decision Trees – Random Forest model – Support Vector Machines – Case Studies.
Mixture Models and EM – K-Means Clustering – Dirichlet Process Mixture Models – Spectral Clustering – Hierarchical Clustering – The Curse of Dimensionality – Dimensionality Reduction – Principal Component Analysis – Latent Variable Models(LVM) – Latent Dirichlet Allocation (LDA) – Case Studies
Bayesian Networks – Conditional Independence – Markov Random Fields – Learning – Naive Bayes Classifiers – Markov Model – Hidden Markov Model- Model evaluation – Precision,Recall.
Reinforcement Learning – Representation Learning – Neural Networks – Active Learning – Ensemble Learning – Bootstrap Aggregation – Boosting – Ada Boost & Gradient Boosting Machines.
Reference Book:
1. Christopher Bishop, “Pattern Recognition and Machine Learningâ€, Springer, 2006. 2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspectiveâ€, MIT Press, 2012. 3. Stephen Marsland, “Machine Learning – An Algorithmic Perspectiveâ€, Second Edition, CRC Press, 2014.
Text Book:
1.Ethem Alpaydin, “Introduction to Machine Learningâ€, Third Edition, Prentice Hall of India, 2015.
