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 (expand [at cse] as in the instructor's email ID above):
Srijon Sarkar (email: srijon [at cse])
Navya Muttineni (email: mnavya [at cse])
Shivam Sood (email: ssood [at cse]
Mayur Warialani (email: mayurwarialani [at cse])
Achari Rakesh Prasanth (email: rakeshprasanth [at cse])
Rishabh Kumar (email: krrishabh [at cse])

Instructor office hours (in CC 221): 4 to 5 pm on Wednesdays

TA office hours: TBA

Course grading

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. Two programming assignments (20%)
  2. Project (10%)
  3. Midsem exam (20%)
  4. Two quizzes(20%)
  5. Final exam (25%)
  6. 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 Title Summary slides Reading
Jan 10, 2020 Machine Learning: What and why? Lecture 1 Chapter 1 of [SS-2017]


Most of the suggested readings will be freely available online. Here are some textbooks to refer to:
  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]
  4. Pattern Recognition and Machine Learning. Christopher Bishop. Springer. 2006. [CB-2006]

Website credit: This is based on a Jekyll template.