Machine Learning

Paper Code: 
MCA 421C
Credits: 
04
Periods/week: 
04
Max. Marks: 
100.00
Objective: 

Course Objectives

This course enables the students to

  1. Understand the basic concepts, approaches and techniques in Machine Learning
  2. Comprehend concept of supervised and unsupervised learning & other advanced learning models.
  3. Understand modern notions in data analysis oriented computing
  4. Learn to evaluate machine learning models
  5. Relate and apply the learned algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models.
  6. Develop the basic skills necessary to pursue research in Machine Learning.

 

 

Course Outcomes(COs):

 

Learning outcomes

(at course level)

Learning and teaching strategies

Assessment

Strategies

CO207. To acquaint with the foundations of machine learning

 

CO208. Understand working concept of different types of supervised models.

 

CO209. Explore Unsupervised learning technique based machine learning

 

CO210. Study different probabilistic graph based models and their applications

 

CO211. Learn advanced machine learning techniques and the problem domain where these can be applied

 

CO212. Understand how to evaluate models generated from data

 

CO213. Apply the algorithms to real-world problems

 

CO214. Interpret & evaluate the results of models learned

 

CO215. Apply gained knowledge to research in Machine Learning

Approach in teaching:

Interactive Lectures,

Modeling, Discussions, implementing enquiry based learning, student centered approach, Research problem based discussions

 

Learning activities for the students:

Experiential Learning, Presentations, Discussions, Quizzes and Assignments

 

Assignments

Written test in classroom

Classroom Activity

Continuous Assessment

Semester End Examination

 

12.00
Unit I: 

Introduction

Machine Learning, Machine Learning Foundations-Overview, Applications, Types of Machine Learning,  Basic Concepts in Machine Learning – Examples of Machine Learning, Perspectives/Issues in Machine Learning, AI vs. Machine Learning, Introduction to Python.

12.00
Unit II: 

Supervised Learning

Introduction, Linear Models of Classification – Decision Trees, Naïve Bayes Classification, Linear Regression – Logistic Regression – Bayesian Logistic Regression – Probabilistic Models Neural Network-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 Component 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 - Naïve Bayes Classifiers – Markov Models – Hidden Markov Models. Undirected graphical Models – Markov Random Fields – Conditional Independence Properties.

12.00
Unit V: 

 

Advanced Learning

Sampling – Basic Sampling Method – Monte Carlo, Reinforcement Learning-Introduction-The Learning Task, Instance based Learning-Nearest neighbor classification, k-nearest neighbor, nearest neighbor error probability, Elements of Reinforcement Learning, Difference between Reinforcement Learning and Supervised Learning, Applications of Reinforcement Learning, Model based learning, Semi-Supervised Learning, Computational Learning Theory.

ESSENTIAL READINGS: 
  • Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer 2006
  • Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
  • Joel Grus, “Data Science from Scratch- First Principles with Python”, O’Reilly, 2015
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
  • M. Gopal, “Applied MACHINE LEARNING”, McGraw-Hill, 2018
  • Mark Summerfield, “Programming in Python 3: A Complete Introduction to the Python Language”, Addison Wesley, 2010
Academic Year: 
2020-21
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