· To understand the basic concept, approaches and techniques in ML
· To develop a deeper understanding of supervised and unsupervised learning.
· To develop the design and programming skills that will help you to build intelligent, adaptive artifacts
To develop the basic skills necessary to pursue research in ML
Introduction
Machine Learning - Machine Learning Foundations –Overview – Applications - Types of machine learning - Basic concepts in machine learning - Examples of Machine Learning.
Supervised 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.
Unsupervised Learning
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.
Probabilistic Graphical Models 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. |
Advanced Learning
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.
· Christopher Bishop, “Pattern Recognition and machine learning”, Springer 2006.
EthemAlpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
· Tom Mitchell, "Machine Learning", McGraw-Hill, 1997.
· Stephen Marsland, “Machine Learning –An Algorithmic Perspective”, CRC Press, 2009
Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
Links:
[1] https://www.csit.iisuniv.ac.in/courses/subjects/machine-learning-5
[2] https://www.csit.iisuniv.ac.in/academic-year/2016-17