COL774: Machine Learning

General Information

Semester: Sem II, 2019-20.

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

TA Assignment Details

Sign up for Piazza
Code: As announced in class.


  • [Aug 16, 2020]: Assignment 4 is out. Due Date: Saturday Sep 5, 2020, 11:50 pm.
  • [Mar 7, 2020]: Assignment 3 (part A) is out. Due Date (for both parts): Tuesday Mar 31, 2020, 11:50 pm.
  • [Mar 6, 2020]: Assignment 2: Updated Due Date: Sunday Mar 8, 11:50 pm.
  • [Feb 27, 2020]: Assignment 2 - Part B is out! Due Date (for both parts): Friday Mar 6, 11:50 pm.
  • [Feb 12, 2020]: Assignment 2 is out! Due Date: Wednesday Mar 4, 11:50 pm.
  • [Jan 21, 2020]: Assignment 1 is out! Due Date: Monday Feb 10, 11:50 pm.
  • [Dec 31, 2019]: Welcome! First Class: Today.

    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!
    Week Topic Supplementary Notes 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 Bishop, Chapter 3.1
    5 Gaussian Discriminant Analysis (GDA), Naive Bayes gda_nb.pdf Bishop, Chapter 4
    6,7 Support Vector Machines svm.pdf Bishop, Chapter 7.1
    8 Decision Trees, Random Forests dtrees.pdf Mitchell, Chapter 3. Online Resource: Random Forests
    9 Neural Networks nnets.pdf nnets-hw.pdf Mitchell, Chapter 4
    10 Deep Learning deep_learning_slides.pdf

    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)

    dec31.pdf, jan1.pdf, jan3.pdf, jan7.pdf, jan8.pdf, jan10.pdf jan14.pdf jan17.pdf jan21.pdf jan22.pdf jan24.pdf jan28.pdf jan29.pdf jan31.pdf feb7.pdf feb11.pdf feb12.pdf feb18.pdf feb19.pdf feb21.pdf feb25.pdf feb26.pdf feb28.pdf

    Class Notes (Date-Wise) for Recorded Lectures

    PPT PDF Video
    mar31_nn.ppt mar31_nn.pdf mar31_nn.mkv
    apr1_nn.ppt apr1_nn.pdf apr1_nn.mkv
    apr3_dnn.ppt apr3_dnn.pdf apr3_dnn.mkv
    apr7_dnn.ppt apr7_dnn.pdf apr7_dnn.mkv
    apr8_dnn.ppt apr8_dnn.pdf apr8_dnn.mkv
    apr10_dnn.ppt apr10_dnn.pdf apr10_dnn.mkv
    apr14_kmeans.ppt apr14_kmeans.pdf apr14_kmeans.mkv
    apr15_gmm.ppt apr15_gmm.pdf apr15_gmm.mkv
    apr17_em.ppt apr17_em.pdf apr17_em.mkv
    apr21_em.ppt apr21_em.pdf apr21_em.mkv
    apr22_em_pca.ppt apr22_em_pca.pdf apr22_em_pca.mkv
    apr24_pca.ppt apr24_pca.pdf apr24_pca.mkv
    apr28_pca_learntheory.ppt apr28_pca_learntheory.pdf apr28_pca_learntheory.mkv
    apr29_learntheory.ppt apr29_learntheory.pdf apr29_learntheory.mkv
    may1_learntheory.ppt may1_learntheory.pdf may1_learntheory.mkv
    may2_learntheory.ppt may2_learntheory.pdf may2_learntheory.mkv

    Help Session Recordings

    Pytorch Help Seesion (Aug 12) (approx size 350 MB)

    Question-Answer Session Recordings

    April 12 (Logistics, Neural Networks And Deep Learning): apr12_video.mp4 (approx size 1.1 GB), apr12_audio.m4a (approx size 46 MB)
    April 19 (Logistics, K-Means, GMM, EM and Earlier): apr19_video.mp4 (coming soon), apr19_audio.m4a (coming soon)
    April 26 (Logistics, EM, PCA and Earlier): apr26_video.mp4 (approx size 63 MB), apr26_audio.m4a (approx size 15 MB)
    May 3 (Logistics, PCA, Learning Theory and Earlier): may3_video.mp4 (approx size 50 MB) may3_audio.m4a (approx size 11 MB)
    May 10 (Logistics, All Online Material):

    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 "2017anz7535" and your name is "Nitika Rao", your submission directory should be named as "2017anz7535_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 a penalty of -10. More severe penalties may follow.
    6. Late Policy: You are allowed a total of 5 late days acorss all the assignments. You are free to decide how you would like to use them. You will get a zero on an assignment once you exceed the (total) allowed exemption of 5 days. This applies to the first 3 assignments in the course (i.e., leaving out the last assigment based on a competition).

    Practice Questions


    1. Assignment 4 [Updated: Saturday Sep 5,2020]
      Due Date: Saturday September 6, 2020. 11:50 pm
    2. Assignment 3 (Both Parts) [Updated: Sunday Apr 19,2020].
      Datasets (Part B):
      Due Date for Part B: Friday April 24, 2020. 11:50 pm
    3. Assignment 3 (Part A)
      Datasets (Part A):
      Due Date for Part A [Updated]: Friday Mar 27, 2020. 11:50 pm
    4. Assignment 2 (Part A), Assignment 2 (Part B)
      Datasets: (Part B)
      Due Date (Updated on Mar 6): Sunday March 8, 2020. 11:50 pm
    5. Assignment 1 [Updated: Jan 29, 2020]
      Due Date: Monday February 10, 2020. 11:50 pm

    Grading Policy (Tentative)

    Assignments Ass 1: 7%, Ass 2: 8.5%, Ass 3: 8.5%, Ass 4: 10%. Total: 34%
    Minors Minor 1: 17.5%, Minor 2: 17.5%. Total: 35%
    [Minor 2 to be possibly held along with Major due to COVID].
    Major 31%