Applications Open now for September 2024 Batch | Applications Close: Sep 15, 2024 | Exam: Oct 27, 2024

Applications Open now for September 2024 Batch | Applications Close: Sep 15, 2024 | Exam: Oct 27, 2024

Diploma Level Course

Machine Learning Techniques

To introduce the main methods and models used in machine learning problems of regression, classification and clustering. To study the properties of these models and methods and learn about their suitability for different problems.

by Arun Rajkumar

Course ID: BSCS2007

Course Credits: 4

Course Type: Data Science

Pre-requisites: None

Co-requisites: BSCS2004 -  Machine Learning Foundations

What you’ll learnVIEW COURSE VIDEOS

Demonstrating In depth understanding of machine learning algorithms - model, objective or loss function, optimization algorithm and evaluation criteria.
Tweaking machine learning algorithms based on the outcome of experiments - what steps to take in case of underfitting and overfitting.
Being able to choose among multiple algorithms for a given task.
Developing an understanding of unsupervised learning techniques.

Course structure & Assessments

12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.

WEEK 1 Introduction; Unsupervised Learning - Representation learning - PCA
WEEK 2 Unsupervised Learning - Representation learning - Kernel PCA
WEEK 3 Unsupervised Learning - Clustering - K-means/Kernel K-means
WEEK 4 Unsupervised Learning - Estimation - Recap of MLE + Bayesian estimation, Gaussian Mixture Model - EM algorithm.
WEEK 5 Supervised Learning - Regression - Least Squares; Bayesian view
WEEK 6 Supervised Learning - Regression - Ridge/LASSO
WEEK 7 Supervised Learning - Classification - K-NN, Decision tree
WEEK 8 Supervised Learning - Classification - Generative Models - Naive Bayes
WEEK 9 Discriminative Models - Perceptron; Logistic Regression
WEEK 10 Support Vector Machines
WEEK 11 Ensemble methods - Bagging and Boosting (Adaboost)
WEEK 12 Artificial Neural networks: Multiclass classification.
+ Show all weeks

Prescribed Books

The following are the suggested books for the course:

Pattern Classification by David G. Stork, Peter E. Hart, and Richard O. Duda

Pattern Recognition and Machine Learning by Christopher M. Bishop

The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

About the Instructors

Arun Rajkumar
Assistant Professor, Department of Computer Sciences & Engineering, IIT Madras

I am currently an Assistant Professor at the Computer Science and Engineering department of IIT Madras. Prior to joining IIT Madras, I was a research scientist at the Xerox Research Center (now Conduent Labs), Bangalore for three years. I earned my Ph.D from the Indian Institute of Science where I worked on 'Ranking from Pairwise Comparisons'. My research interests are in the areas of Machine learning, statistical learning theory with applications to education and healthcare.


Other courses by the same instructor: BSCS2004 - Machine Learning Foundations