E0 270: Machine Learning, January Term 2018

E0 270: Machine Learning   (Jan-April 2018)

Department of Computer Science & Automation
Indian Institute of Science

[Announcements]  [Course Description]  [Assignments]  [Academic Honesty]  [Tutorials/Discussions]  [Schedule] 

We have sent an invitation mail for Canvas. Please follow the instructions given in that email to create an account. Make sure that you edit your profile and enter your name after creating the account.

Feel free to write to [shubhamg@iisc] if you have not received the invitation (check your spam folder first, if you are using a Focussed Inbox on Outlook then check the 'Others' tab in inbox) or if you have any other difficulties logging in.

We will make all subsequent announcements via Canvas, hence registration is mandatory for people who are crediting the course.

Course Information

Class Meetings

Lectures: Tue-Thu 11:00am-12:30pm, CSA 117
First lecture: Tue Jan 2

Tutorial/discussion sessions will be scheduled on an on-going basis.


Prof. Chiranjib Bhattacharyya (chiru@iisc)
Prof. Ambedkar Dukkipati (ad@iisc)


Abhinav Anand [abhinava@iisc]
Annervaz K M [annervaz@iisc]
Shubham Gupta [shubhamg@iisc]
Sweta Sharma [swetasharma@iisc]

TA Hours

Please take appointment over email from the TA


Course Description

With the increasing amounts of data being generated in diverse fields such as astronomical sciences, health and life sciences, financial and economic modeling, climate modeling, market analysis, and even defense, there is an increasing need for computational methods that can automatically analyze and learn predictive models from such data. Machine learning, the study of computer systems and algorithms that automatically improve performance by learning from data, provides such methods; indeed, machine learning techniques are already being used with success in a variety of domains, for example in computer vision to develop face recognition systems, in information retrieval to improve search results, in computational biology to discover new genes, and in drug discovery to prioritize chemical structures for screening. This course aims to provide a sound introduction to both the theory and practice of machine learning, with the goal of giving students a strong foundation in the subject, enabling them to apply machine learning techniques to real problems, and preparing them for advanced coursework/research in machine learning and related fields.

Course Evaluation (Tentative)

1st Mid Term - Feb 15
2nd Mid Term - March 20


Recommended textbooks: Additional textbooks: (Optimization texbooks)

Preferred background

E0 232: Probability and Statistics (or equivalent course elsewhere) and earned a grade of B or higher. In addition, some background in linear algebra and optimization will be helpful.


As part of the this course, you are required to work on a project. This will give you hands-on experience of working with data from various domains - text, images, videos etc that are used in the contemporary ML research, and also expose you to some specialized topics of ML that are more advanced or recent than what will be covered in the lectures.

A list of project ideas will be mailed on the mailing list. You can also come up with your own projects ideas.

Project Policy


Assignment Policy

The following assignment policy will be strictly followed: * Except in the case of a documented medical/personal emergency, which must be supported by a medical certificate or signed letter submitted to the instructors and to the CSA office.

Academic Honesty

As students of IISc, we expect you to adhere to the highest standards of academic honesty and integrity.

Assignments in the course are designed to support your learning of the subject. Copying will not help you (in the exams or in the real world), so don't do it. If you have difficulties learning some of the topics or lack some background, try to form study groups where you can bounce off ideas with one another and try to teach each other what you understand. You're also welcome to talk to any of us and we'll be glad to help you.

If any assignment/exam is found to be copied, it will automatically result in a zero grade for that assignment/exam and a warning note to your advisor. Any repeat instance will automatically lead to a failing grade in the course.


  Date Topic Lecture Notes Additional Recommended Reading Notes
1 Jan 2 Organisational Meeting
2 Jan 4 Introduction to Machine Learning, Classification using Bayes rule NA Duda and Hart: Chapter 2
3 Jan 11 Introduction to Bayes Decision Theory NA Duda and Hart: Chapter 2
4 Jan 16 Bayes Decision Theory (Contd...) NA Duda and Hart: Chapter 2
5 Jan 18 Learning as Optimization, Linear Regression NA Bishop: Chapter 3
6 Jan 23 Probabilistic view of Linear Regression: ML and MAP estimates NA NA
7 Jan 30 Logistic Regression: Optimization and Probabilistic approaches NA NA
8 Feb 1 Parameter estimation for Logistic Regression: Gradient Descent, Newton Method, Stochastic Gradient methods NA NA
9 Feb 6 Hyperplane based classifiers, Perceptron, and Perceptron Convergence Theorem NA NA
10 Feb 9 Introduction to Support Vector Machines NA NA
11 Feb 13 Support Vector Machines NA NA
12 Feb 20 Kernel Methods and Kernel SVM NA NA
13 Feb 22 Feedforward Neural Network and Backpropagation Algorithm NA NA
14 Feb 28 Undirected Graphical Models, Markov Random Fields NA An Introduction to Restricted Boltzmann Machines
15 March 4 Introduction to MCMC and Gibbs Sampling NA An Introduction to Restricted Boltzmann Machines
16 March 6 Restricted Boltzmann Machine NA An Introduction to Restricted Boltzmann Machines
A Practical Guide to Training Restricted Boltzmann Machines
17 March 8 Convolutional Neural Networks and Recurrent Neural Networks NA NA
18 March 13 Boltzmann Machine and Variational methods NA https://onlinelibrary.wiley.com/doi/abs/10.1207/s15516709cog0901_7
Boltzmann Machines
19 March 15 Variational methods (contd...) NA Variational methods (See Section 6.3.1)
20 March 22 EM algorithm, Mixture models and K-means NA Bishop: Chapter 9
21 March 27 Bayesian Networks, Introduction to HMMs NA Tutorial on HMM
22 April 2 HMMs (contd...) - Forward Backward algorithm NA Tutorial on HMM