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
Recommended Pre-requisites: None
Recommended Co-requisites: BSCS2004 - Machine Learning Foundations
What you’ll learn VIEW 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 to machine learning; Supervised vs unsupervised, batch vs online, instance-based vs model-based; Problems - regression, classification, clustering; Challenges |
| WEEK 2 | Models of regression; Linear regression - least squares; Polynomial regression - learning curves; Regularized linear models - Ridge, LASSO |
| WEEK 3 | Models of regression; Linear regression - least squares; Polynomial regression - learning curves; Regularized linear models - Ridge, LASSO |
| WEEK 4 | Models of classification; Discriminant functions and decision boundaries - two classes, multiple classes, least squares, perceptron; Probabilistic generative and discriminative models - ML, Naive Bayes, exponential family, logistic regression |
| WEEK 5 | Models of classification; Discriminant functions and decision boundaries - two classes, multiple classes, least squares, perceptron; Probabilistic generative and discriminative models - ML, Naive Bayes, exponential family, logistic regression |
| WEEK 6 | Models of classification; Discriminant functions and decision boundaries - two classes, multiple classes, least squares, perceptron; Probabilistic generative and discriminative models - ML, Naive Bayes, exponential family, logistic regression |
| WEEK 7 | Models of classification; Nearest Neighbours - regression and classification problems |
| WEEK 8 | Support Vector Machines; Linear SVM - soft margin classification; Nonlinear SVM - kernels |
| WEEK 9 | Decision Trees, Ensemble Methods and Random Forests; Training decision trees, making predictions; Bagging, Boosting |
| WEEK 10 | Decision Trees, Ensemble Methods and Random Forests; Training decision trees, making predictions; Bagging, Boosting |
| WEEK 11 | Clustering; k-Means - algorithm, demo and how to select k HAC |
| WEEK 12 | Neural networks; Multi-layer perceptron, activation functions; Training - SGD and back propagation; Hyperparameters - number of layers, neurons, activation functions; Note: We will give additional material for NN and Clustering. |
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
Dr. Ashish Tendulkar is a researcher with Google Research Bangalore. He holds Masters and PhD from IIT Bombay. Before his current position, he was an Assistant Professor at IIT Madras and Head of data sciences at Persistent Systems Pune. Ashish is passionate about teaching ML and writing AI related contents in Indian languages.
Other courses by the same instructor: BSCS2008 - Machine Learning Practice