Course Description

Welcome to "Introduction to Machine Learning 419(M)".
In this undergraduate-level course, you will be introduced to the foundations of machine learning along with a slew of popular machine learning techniques. This will also give you insights on how to apply machine learning to solve a new problem.

This course is open to any non-CSE undergraduate student who wants to do a minor in CSE. There are no prerequisites.

Course Info

Time: Wednesdays, Fridays, 9.30 am to 10.55 am
Venue: CC 103
Instructor: Preethi Jyothi. You can email me at pjyothi [at] cse [dot] iitb [dot] ac [dot] in

TAs: Saurabh Garg, Tanmay Parekh, Sunandini Sanyal, Anupama Vijjapu, Himanshu Agarwal, Aniket Muiri, Pooja Palod
Instructor office hours (in CC 221): 5 pm to 6 pm on Fridays
TA office hours (in KreSIT library):
Himanshu, Aniket and Pooja (3 pm to 4 pm on Wednesdays)
Saurabh and Tanmay (7 to 8 pm on Thursdays)
Sunandini and Anupama (10.45 am to 11.45 am on Mondays)

Course grading

All assignments should be completed individually. No form of collaboration is allowed unless explicitly permitted by the instructor. Anyone deviating from these standards of academic integrity will be reported to the department's disciplinary committee.

  1. Four or five assignment sets (40%)
  2. Midsem exam (15%)
  3. Project (15%)
  4. Final exam (25%)
  5. Participation (5%)


Course syllabus includes basic classification/regression techniques such as Naive Bayes', decision trees, SVMs, boosting/bagging and linear/logistic regression, maximum likelihood estimates, regularization, basics of statistical learning theory, perceptron rule/multi-layer perceptrons, backpropagation, brief introduction to deep learning models, dimensionality reduction techniques like PCA and LDA, unsupervised learning: k-means clustering, gaussian mixture models, selected topics from natural/spoken language processing, computer vision, etc. [The syllabus is subject to minor changes depending on how the course proceeds.]


Date Lecture Title Slides Reading
Jan 5, 2018 Machine Learning: What and why? Lecture1.pdf Chapter 1 of [SS-2017]
Jan 10, 2018 Linear Regression (Part I) Lecture2.pdf Chapter 3.1,3.2.1 of [TH-2009]
Jan 12, 2018 Linear Regression (Part II) TBA TBA
Jan 16, 2018 Assignment 1 released:
Due Jan 24, 2018
Assignment 1 -
Jan 17, 2018 Linear Classification:
Perceptron Algorithm


All the suggested readings will be freely available online. We will use (parts of) the following three online textbooks:
  1. Understanding Machine Learning. Shai Shalev-Shwartz and Shai Ben-David. Cambridge University Press. 2017. [SS-2017]
  2. The Elements of Statistical Learning. Trevor Hastie, Robert Tibshirani and Jerome Friedman. Second Edition. 2009. [TH-2009]
  3. Foundations of Data Science. Avrim Blum, John Hopcroft and Ravindran Kannan. January 2017. [AB-2017]

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