Mid Term: 20 Marks
Assignments: 30 Marks
Participation in the Discussion and Doubt Solving: 10 Marks
Project mid evaluation: 10 Marks
Project Final evaluation: 30 Marks
Announcements
Classes will be held online on Teams meeting for the first month tentatively.
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.
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 students of IISc, we expect you to adhere to the highest standards of academic honesty and integrity.
Elements of 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 exam/report is found to be copied, it will automatically
result in a zero grade for that exam/project and a warning note to
your advisor. Any repeat instance will automatically lead to a failing
grade in the course.
Goodfellow, Bengio, Courville, Deep Learning, MIT Press, 2017
Additional textbooks:
T. Hastie, R. Tibshirani and J. Friedman, The Elements of
Statistical Learning: Data Mining, Inference and Prediction. Springer,
2nd Edition, 2009.
[free download available]
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
Some projects ideas are based on reading and survey of several papers, and others are based on implementation of one or two papers, and comparison of the methods proposed in them over datasets. We will point you to relevant papers and datasets. You can read/implement other relevant papers and datasets if you want. You can also innovate and come up with your own approaches for solving the problems.
The best grades will be reserved for the groups which come up with some new approaches, either theoretically or experimentally, which have not been used in the literature.
Each project will have an associated project mentor. He/she will help you to locate papers and datasets, and clear doubts.
In implementation-based projects, it may so happen that codes are already available online. It is perfectly fine to use them. Only, that should be cited in the reports.
You are required to provide a mid-term report around the third week of March, and the final report shortly before the final examination.
Plagiarism is strictly prohibited while writing the reports. Any plagiarism detected will result in F grade for the course.