Machine Learning

Learning Outcomes: 
After successful completion of the course students should be able to
• Understand a set of well-known supervised, unsupervised and semi-supervised learning algorithms
• Use a tool to implement typical clustering algorithms for different types of applications
• Identify applications suitable for different types of machine learning with suitable justification
• Implement probabilistic discriminative and generative algorithms for an application of your choice and analyze the results
• Design and implement an HMM for a sequence model type of application
• Design a neural network for an application of your choice
Syllabus: 
Unit NoTopics
1

Introduction:

Machine Learning - Machine Learning Foundations –Overview – Design of a Learning system - Types of machine learning –Applications Mathematical foundations of machine learning - random variables and probabilities - Probability Theory – Probability distributions -Decision Theory- Bayes Decision Theory - Information Theory

2

Supervised Learning:

Linear Models for Regression - Linear Models for Classification – Naïve Bayes - Discriminant Functions -Probabilistic Generative Models -Probabilistic Discriminative Models - Bayesian Logistic Regression. Decision Trees - Classification Trees- egression Trees - Pruning. Neural Networks -Feed-forward Network Functions - Back- propagation. Support vector machines - Ensemble methods- Bagging- Boosting.

3

Unsupervised Learning:

Clustering- K-means - EM Algorithm- Mixtures of Gaussians-The Curse of Dimensionality - Dimensionality Reduction – Factor Analysis - Principal component Analysis -  Probabilistic PCA – Independent Analysis

4

Probabilistic Graphical Models:

Graphical Models - Undirected graphical models - Markov Random Fields - Directed Graphical Models -Bayesian Networks - Conditional independence properties - Inference – Learning-Generalization - Hidden Markov Models - Conditional random fields(CRFs)

5

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 Computational Learning Theory - Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting.

Text Books: 
Name : 
Pattern Recognition and Machine Learning
Author: 
Christopher Bishop
Publication: 
Springer, 2007.
Name : 
Machine Learning: A Probabilistic Perspective
Author: 
Kevin P. Murphy
Publication: 
MIT Press, 2012.
Reference Books: 
Name: 
Introduction to Machine Learning
Author: 
Ethem Alpaydin
Publication: 
MIT Press
Edition: 
Third Edition, 2014.
Name: 
Machine Learning
Author: 
Tom Mitchell
Publication: 
McGraw-Hill
Edition: 
1997
Name: 
The Elements of Statistical Learning
Author: 
Trevor Hastie, Robert Tibshirani, Jerome Friedman
Publication: 
Second Edition, 2011
Name: 
Machine Learning - An Algorithmic Perspective
Author: 
Stephen Marsland
Publication: 
Chapman and Hall/CRC Press
Edition: 
Second Edition, 2014
Syllabus PDF: 
AttachmentSize
PDF icon Machine learning.pdf184.6 KB
branch: 
CBA
BDA
MA
Course: 
2014
Stream: 
B.Tech