Machine Learning
ES 654 · IIT Gandhinagar · Semester II, 2021–22
ES 654 introduces machine learning: metrics, decision trees and ensembles, linear regression with gradient descent, ridge and lasso, Bayesian learning and logistic regression, neural networks and CNNs, naive Bayes, support vector machines and unsupervised learning. Every lecture links its slides, and most link a class recording.
- Instructor
- Prof. Nipun Batra
- nipun.batra@iitgn.ac.in
- Classes
- Monday 10:05–11:00 am, Wednesday 9:05–10:00 am, Thursday 10:05–11:00 am
- Online
- Zoom link
- Google Classroom
- Code jclv62l
- Teaching assistants
- 4 TAs
- Prerequisites
- Good experience in Python programming, probability and linear algebra
This offering ran from January to April 2022. Its slides, class recordings and quiz papers remain available.
- Lectures
- 39
- Recordings
- On Google Drive
- Quiz papers
- 2
- Policies
- FAQ and grading
Lectures
- 1–4Introduction and metrics4 lectures
- 5–12Decision trees and ensembles8 lectures
- 13–25Linear regression and optimisation13 lectures
- 26–31Bayesian learning and logistic regression6 lectures
- 32–35Neural networks4 lectures
- 36–39Naive Bayes, SVMs and unsupervised learning4 lectures
Questions
Where are the grading policy and the course FAQ?
In the course FAQ and grading policy document. You have comment access: comment on it to ask for more details.
Where can I find project ideas?
In the list of project ideas. You have comment access: comment on it to ask the TAs for more details.
What are the prerequisites?
Good experience in Python programming, probability and linear algebra.
How do I prepare before the course?
Refresh the prerequisites with the first four chapters of the Python Data Science Handbook, some material on linear algebra and Khan Academy's course on statistics and probability.
Where are the class recordings?
Each lecture on the Lectures page links its recording where one exists, and all of them are in a Google Drive folder. Spreadsheets used in class are in another folder.
References
Reference textbooks:
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R (online)
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006 (free PDF)
- Jerome Friedman, Trevor Hastie and Robert Tibshirani, The Elements of Statistical Learning, Springer, 2001 (free PDF)
- Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification, John Wiley & Sons, 2012
- Tom M. Mitchell, Machine Learning, McGraw Hill, 1997
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
- Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016 (online)
Other machine learning courses:
- NPTEL course by Balaraman Ravindran
- CMU course by Tom Mitchell and Maria-Florina Balcan
- Coursera course by Andrew Ng
- fast.ai course on ML and Practical deep learning for coders
- Course by Alex Ihler, UCI
Teaching assistants
- Zeel Patel
- Rishiraj Adhikaryrishiraj.a@iitgn.ac.in
- Shriraj Sawant
- Hetvi Shastri