Machine Learning

Paper Code: 
MCA 525E
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

·         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

10.00
Unit I: 

Introduction

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

12.00
Unit II: 

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.

12.00
Unit III: 

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.

12.00
Unit IV: 

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.

 

14.00
Unit V: 

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.

ESSENTIAL READINGS: 

·         Christopher Bishop, “Pattern Recognition and machine learning”, Springer 2006.

EthemAlpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005 

REFERENCES: 

·         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 

Academic Year: 
2016-17
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