Machine Learning

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

 The course will enable 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 Learning Outcomes (CLOs):

 

Learning Outcome (at course level)

Students will be able to:

Learning and teaching strategies

Assessment Strategies

  1. To acquaint with the foundations of machine learning
  2. Understand working concept of different types of supervised models.
  3. Explore Unsupervised learning technique based machine learning
  4. Study different probabilistic graph based models and their applications
  5. Learn advanced machine learning techniques and the problem domain where these can be applied
  6. Understand how to evaluate models generated from data
  7. Apply the algorithms to real-world problems
  8. Interpret & evaluate the results of models learned
  9. 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 – 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. Experiments of Classification using Python.

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. Experiments of Clustering using Python.

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, 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: 
  1. Christopher Bishop, “Pattern Recognition and Machine Learning”, Springer 2006
  2. EthemAlpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005
  3. Joel Grus, “Data Science from Scratch- First Principles with Python”, O’Reilly, 2015
REFERENCES: 
  1. Tom Mitchell, “ Machine Learning”, McGraw-Hill, 1997
  2. Stephen MarsLand, “Machine Learning-An Algorithmic Perspective”, CRC Press, 2009
  3. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012
  4. M. Gopal, “Applied MACHINE LEARNING”, McGraw-Hill, 2018
  5. Mark Summerfield, “Programming in Python 3: A Complete Introduction to the Python Language”, Addison Wesley, 2010
Academic Year: 
2019-20
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