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.
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
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. |
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
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