Machine Learning

Machine Learning - Machine Learning Foundations –Overview – Applications - Types of machine learning - Basic concepts in machine learning - Examples of Machine Learning.

Introduction, Linear Models for Classification – Linear Regression – Logistic Regression - Bayesian Logistic Regression - Probabilistic Models. Neural Networks -Feed-forward Network Functions - Error Back propagation - Regularization - Bayesian Neural Networks - Radial Basis Function Networks. Ensemble methods- Random Forest - Bagging- Boosting

Clustering- K-means clustering – EM (Expectation–Maximization) - Mixtures of Gaussians - EM Algorithm in General - The Curse of Dimensionality -Dimensionality Reduction - Factor analysis - Principal Component Analysis - Probabilistic PCA- Independent components analysis. Challenges for big data analytics

Directed Graphical Models - Bayesian Networks - Exploiting Independence Properties - From Distributions to Graphs -Examples -Markov Random Fields - Inference in Graphical Models - Learning –Naive Bayes classifiers-Markov Models – Hidden Markov Models. Undirected graphical models- Markov Random Fields- Conditional independence properties

Sampling – Basic sampling methods – Monte Carlo. Reinforcement Learning- K-Armed Bandit Elements - Model-Based Learning- Value Iteration- Policy Iteration. Temporal Difference Learning Exploration Strategies- Deterministic and Non-deterministic Rewards and Actions- Eligibility Traces Generalization- Partially Observable States- The Setting- Example. Semi - Supervised Learning. Computational Learning Theory.