COL774: Machine Learning

General Information

Semester: Sem I, 2022-23.

Instructor: Parag Singla (email: parags AT

Class Timings (Slot C):
  • Tue, 8:00 am - 8:50am
  • Wed, 8:00 am - 8:50am
  • Fri, 8:00 am - 8:50am
Venue: LHC 316

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Code: As (will be) announced in class.

Sign up for Enrolling in the Course (If not already enrolled): Click here
  1. Only students from CSE/EE/Maths/SIT/ScAI should fill this (unless I have confirmed an exception over email).
  2. Students who are only planning to sit through this course Should NOT fill this form.

New:TA Assignment


  • [Oct 28, 2022]: Assignment 4 is out. Due Date: Monday, November 28, 2022, 11:50 pm.
  • [Oct 16, 2022]: Make-up class held on Saturday Oct 16, 2022. Recorded lecture is available from course contents below (topic #10).
  • [Oct 6, 2022]: Assignment 3 is out. Due Date: Wednesday, October 26, 2022, 11:50 pm.
  • [Sep 10, 2022]: Assignment 2 is out. Due Date: Tuesday, October 4, 2022, 11:50 pm.
  • [Aug 27, 2022]: Make-up class held on Saturday Aug 27, 2022. Recorded lecture is available from course contents below (topic #4).
  • [Aug 23, 2022]: Assignment 1 is out. Due Date: Friday, September 9, 2022, 11:50 pm.
  • [Aug 4, 2022]: Welcome! First Class of COL 774 will be on Friday Aug 5. 8 am - 9 am.

    Course Objectives

    (a) To familiarize with/develop the understanding of fundamental concepts of Machine Learning (ML)
    (b) To develop the understanding of working of a variety of ML algorithms (both supervised as well as unsupervised)
    (c) To learn to apply ML algorithms to real world data/problems
    (d) To update with some of the latest advances in the field

    Course Content

    NOTE: The exact list of topics below is tentative (until we are past that week). We will update it as we go through the lectures in each week. So, stay tuned!
    (GANs, Dropouts)
    Week Topic Supplementary Notes
    (by Andrew Ng and Others)
    Book Chapters
    1 Introduction
    2,3 Linear Regression (and Its Variants) lin-log-reg.pdf Bishop, Chapter 3.1
    4 Logistic Regression, Generalized Linear Models lin-log-reg.pdf
    Lecture Video: Saturday, Aug 27
    Bishop, Chapter 3.1
    5 Gaussian Discriminant Analysis (GDA), Naive Bayes gda_nb.pdf Bishop, Chapter 4
    6,7 Support Vector Machines svm.pdf
    Lecture Video: Saturday, Sep 17
    Bishop, Chapter 7.1
    8 Decision Trees, Random Forests dtrees.pdf Mitchell, Chapter 3.
    Online Resources: Random Forests,
    Gradient Boosting - Wikipedia,
    Paper by Friedman (2001) (up to Section 4.5)
    9 Neural Networks nnets.pdf nnets-hw.pdf Mitchell, Chapter 4
    10 Deep Learning deep_learning_slides.pdf
    Lecture Video: Saturday Oct 15
    Lecture Video: Saturday Oct 22
    Additional/Self Reading (Watch 31:15 onward)
    Online Resource:
    Convolutional Neural Networks
    11 K-Means, Gaussian Mixture Models kmeans.pdf gmm.pdf em.pdf pca.pdf
    12 Expectation Maximiation (EM), Principal Component Analysis (PCA) kmeans.pdf gmm.pdf em.pdf pca.pdf
    13 Learning Theory, Model Selection theory.pdf model.pdf Mitchell, Chapter 7
    14 Advanced Topics

    Class Notes (Date-Wise):

    Aug 5, Aug 6,Aug 10, Aug 12, Aug 16, Aug 17 Aug 23, Aug 24, Aug 26, Aug 27, Aug 30, Aug 31, Sep 2, Sep 6, Sep 7, Sep 7, Sep 9, Sep 13, Sep 14, Sep 16, Sep 17, Sep 20, Sep 21,Sep 23, Oct 7, Oct 8, Oct 11, Oct 12, Oct 15, Oct 18, [Oct 19: slides, hand-written], Oct 21, Oct 22,Oct 25, Oct 26,Oct 28, Nov 1, Nov 2,Nov 4,Nov 9, Nov 11,Nov 12

    Video Lectures from Previous offering can be acccessed here COL 774, Sem I, 2021-22 Course Page (Search for Videos)

    Additional Resources

    Review Material

    Topic Notes
    Probability prob.pdf
    Linear Algebra linalg.pdf
    Gaussian Distribution gaussians.pdf
    Convex Optimization (1) convex-1.pdf


    1. Machine Learning: A Probabilistic Perspective. Kevin Murphy. MIT Press, 2012.
    2. Pattern Recognition and Machine Learning. Christopher Bishop. First Edition, Springer, 2006.
    3. Pattern Classification. Richard Duda, Peter Hart and David Stock. Second Edition, Wiley-Interscience, 2000.
    4. Machine Learning. Tom Mitchell. First Edition, McGraw-Hill, 1997.

    Assignment Submission Instructions

    1. You are free to discuss the assignment problems with other students in the class. But all your code should be produced independently without looking at/referring to anyone else's code.
    2. Python is the default programming languages for the course. You should use it for programming your assignments unless otherwise explicitly allowed.
    3. Code should be submitted using Moodle Page. Make sure to include commenrs for readability.
    4. Create a separate directory for each of the questions named by the question number. For instance, for question 1, all your submissions files should be put in the directory named Q1 (and so on for other questions). Put all the Question sub-directories in a single top level directory. This directory should be named as "yourentrynumber_firstname_lastname". For example, if your entry number is "2020cs19535" and your name is "Nitika Rao", your submission directory should be named as "2020cs19535_nitika_rao". You should zip your directory and name the resulting file as "" e.g. in the above example it will be "". This single zip file should be submitted online.
    5. Honor Code: Any cases of copying will be awarded a zero on the assignment and an additional penalty equal to the negative of the total weightage of the assignment. More severe penalties may follow.
    6. Late Policy: You are allowed a total of 5 late (buffer) days acorss the first 3 assignments. You are free to decide how you would like to use them. The late policy (if any) for the last assignment will be announced separately. You will get a penalty of 10% deduction in marks (per day) for every additional late day in submission used beyond the allowed 5 buffer days (applicable to first 3 assignments only).

    Practice Questions


    1. Assignment 4
      Due Date: Monday November 28, 2022, 11:50 pm.
    2. Assignment 3 [Updated: Friday Oct 14, 8:15 AM]
      Datasets: Available from the links given in the assignment pdf
      Due Date: Wednesday October 26, 2022. 11:50 pm
    3. Assignment 2 [Updated: Sunday Sep 18, 9:00 AM].
      Datasets: Available from the link given in the assignment pdf
      Due Date:Tuesday October 4, 2022. 11:50 pm
    4. Assignment 1
      Due Date:Friday September 9, 2022. 11:50 pm

    Grading Policy (Tentative)

    Assignments (4) Ass1: 7%. Ass2: 9%, Ass3: 9%, Ass4: 10 %. [Total Assignment Weight: 35%]
    Minor 25%
    Major 40%