Learning Objectives

This is an intermediate level course in computer science field and assumes background in algorithm, programming and introductory knowledge of probability and linear algebra. The main objective of the course is to introduce student with methodologies and applications of various topics of machine learning. The course will also provide sufficient background to motivate students to take up advanced levels courses related to machine learning, deep learning, bioinformatics, robotics, Artificial intelligence etc.

Learning outcome

Upon successful completion, the students will be able to implement few machine learning models like regression classification and clustering and will be able to learn models from data and evaluate models on test data.

Syllabus

Introduction: recap of linear algebra(vector derivative) and probability theory (Bayes Rule, Parameter Estimation (ML, MAP), ) basics. [4 lectures]

Regression: Linear Regression, Ridge Regression [3 lectures]

Non-parametric Methods: k-Nearest Neighbours, Parzen Window.[2 lecture]

Discriminative Learning models: LDA, Logistic Regression, Perceptrons, Support Vector Machines. [6 lectures]

Dimensionality Reduction: Principal Component Analysis, Fischer’s Discriminant Analysis. [4 lectures]

Evaluation and Model Selection: ROC Curves, Evaluation Measures, Cross validation. [2 lectures]

Decision Trees: Splitting Criteria, CART. [4 lectures]

Ensemble Methods: Boosting, Bagging, Random Forests. [4 lectures]

Clustering: Partitional, Hierarchical, density based clustering. [4 lectures]

Sequential Learning: Hidden Markov Model [4 lectures]

Neural Network: Feedforward NN, Back Propagation. [4 lectures]

Text books

  1. Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. ISBN 0387952845.

  2. Richard Duda, Peter Hart, David Stork. Pattern Classification, 2nd Ed.,, John Wiley & Sons, 2001. ISBN 9788126511167.

Refferenes

  1. Tom Mitchell. Machine Learning. ISBN 0070428077.

  2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. Introduction to Statistical Learning, Springer, 2013. ISBN 9781461471387

  3. Anand Rajaraman, Jurij Leskovec, and Jeffrey Ullman. Mining of Massive Datasets. Cambridge University Press. 2012. (free online)

  4. Sam Roweis’s probability review

  5. Sam Roweis’s linear algebra review

  6. Convex Optimization by Stephen Boyd and Lieven Vandenberghe. (Can be downloaded as PDF file.)

  7. Christopher Bishop. Pattern Recognition and Machine Learning. ISBN 0387310738.

  8. Shai Shalev-Shwartz, and Shai Ben-David. Understanding Machine Learning:From Theory to Algorithms, Cambridge University Press, 2014.

Meta Data

  • Proposing Faculty : Dr Sahely Bhadra
  • Department / Centre : Computer Science and Engineering
  • Programme : B.Tech
  • Proposal Type: Revised
  • Offerings

Past Offerings

  • Offered in July-Dec, 2018 by Sahely
  • Offered in July-Dec, 2017 by Sahely