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

Practical Content :-

  •          Basic concepts of data processing using mean, median, standard deviation, variance, covariance, hypothesis testing.
  •          KNN algorithm for classification problems
  •          Linear Regression and multiple linear regression for modeling the relationship between a scalar dependent variable and one or more independent variables
  •          Naïve Bayesian classifier for supervised classification
  •          The various activation functions for ANN
  •          single-layer perceptron Learning and testing Algorithm for prediction
  •          Multilevel perceptron Learning and testing Algorithm for prediction
  •          Back-propagation Learning and testing Algorithm for prediction
  •          K-Means for clustering
  •          Principal component analysis for dimensionality reduction
  •         KSOM for dimensionality reduction and unsupervised classifier
  •        Monte Carlo method of board class of computational algorithms that rely on repeated random sampling to obtain numerical results
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

Dimensionality Reduction

Introduction, Feature Selection and Feature Extraction, Principle Component Analysis, Linear Discriminant Analysis, Factor Analysis

4

Neural Network and Support Vector Machine:

Introduction to NN, Single Layer Perceptron, Multi-Layer Perceptron, Feed-forward Network Functions, Back- Propagation, Multi-Layer Perceptron with Gradient Descent. Support Vector Machines – Optimal Separation, Kernels 

5

Unsupervised Learning:

Introduction to Clustering, Advanced Clustering Methods, Cluster Evaluation

6

Advance Topics:

RNN, CNN, Overview of reinforcement learning, markov decision processes, Q-Learning & SARSA Learning, Applications of Reinforcement Learning.

 

Self Study

Evolutionary Learning, Markov chain monte carlo methods, GMM.

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: 
EthemAlpaydin
Publication: 
MIT Press
Edition: 
3rd, 2014
Name: 
Machine Learning
Author: 
Tom Mitchell
Publication: 
McGraw-Hill, 1997.
Name: 
The Elements of Statistical Learning
Author: 
Trevor Hastie, Robert Tibshirani
Jerome Friedman
Publication: 
Springer
Edition: 
2nd , 2011
Name: 
Machine Learning - An Algorithmic Perspective
Author: 
Stephen Marsland
Publication: 
Chapman and Hall/CRC Press
Edition: 
2nd , 2014
Syllabus PDF: 
AttachmentSize
PDF icon ML.pdf229.16 KB
branch: 
CBA
BDA
MA
Course: 
2018
Stream: 
B.Tech