Teaching Assistants
Parag D [paragdutta@iisc.ac.in]
Arpana A [arpanaalka@iisc.ac.in]
Rahul V [rahulv@iisc.ac.in]
Shaarad A R [rangaa@iisc.ac.in]
Class Meetings
Lectures: Monday, Wednesday 11:00am-12:30pm CSA 117
First lecture:
Jan 3, 2024 [Tutorial/discussion sessions will be scheduled on an
on-going basis.]
Instructor: Prof. Ambedkar Dukkipati (ambedkar@iisc.ac.in)
Course Evaluation (Final)
Mid Term: 1 * 20 Marks
Assignments: 2 * 15 Marks (Programming)
End Term: 50 Marks
Bonus Assignment: TBD
Course Teams Group
Code to join group: 63rvo5z (Instructions)
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.
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.
| Topics | Lecture Notes | Remarks | |
| 1 | Introduction, What is Data and Model, Machine Learning Workflow, Distance Based Classifiers, Bayes Decision Theory | slides1 | |
| 2 | Different types of Learning, Supervised Learning, Foundational Aspects of ML, Linear Regression | slides2 | |
| 3 | Probabilistic view of Linear Regression, Logistic Regression, Hyperplane based Classifiers and Perceptron | slides3 | |
| 4 | Support Vector Machines, Kernel Methods | slides4 | |
| 5 | Feed Forward Neural Networks, Backpropagation algorithm, CNNs, RNNs | slides5 | |
| 6 | Unsupervised Learning, Dimentionality Reduction, K-Means Clustering | slides6 | |
| 7 | Spectral Clustering | slides7 |
| Topics | Date | TA | |
| 1 | Naive Bayes and Bayes Decision Theory | January | Shaarad R. A. |
| 2 | Linear Regression and Gradient Descent | February | Parag D. |
| 3 | Support Vector Machines | -- | Shaarad R. A. |
| 4 | Perceptrons, Neural Networks (MLPs, CNNs), Backpropagation, Vanishing Gradients, Kaiming Initialization, BatchNorm, Dropouts | March | Parag D. |
| 5 | Generative modeling - GANs and VAEs, Conditional VAEs | April | Ayyoob M. |
Recommended Textbooks:
Additional Textbooks:
Optimization Textbooks: