COL774: Machine Learning

General Information

Semester: Sem II, 2017-18.

Instructor: Parag Singla (email: parags AT

Class Timings (Slot F):
  • Tue, 11:00 am - 11:50am
  • Thu, 11:00 am - 11:50am
  • Fri, 11:00 am - 11:50am
Venue: LHC 512.

Teaching Assistants (TAs)

TA Assignment

Sign up for Piazza
Code: As Announced in Class.


  • [Apr 1, 2018]: Complete Assignment 3 is out now. Due Date: Sunday April 8, 11:50 pm.
  • [Mar 23, 2018]: Class notes until Mar 20 are now posted below.
  • [Mar 15, 2018]: Assignment 3 is now posted below. Deadline: Sunday Apr 8, 11:50 pm.
  • [Feb 25, 2018]: Complete Assignment 2 is now posted below. Deadline: Sunday Mar 11, 11:50 pm.
  • [Feb 1, 2018]: Class notes until Jan 25 have been posted.
  • [Feb 1, 2018]: Last year's Minor 1 exam has been posted for additional practice.
  • [Jan 24, 2018]: TA Assignment is now up on the website. Check it out!
  • [Jan 20, 2018]: Assignment 1 is out! Due Date: Monday Feb 12. 11:50 pm.
  • [Jan 6, 2018]: Welcome to COL 774 - Graduate Course on Machine Learning!

    Course Content

    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)

    jan18.pdf, jan19.pdf, jan23.pdf, jan25.pdf, jan30.pdf, feb1.pdf, feb9.pdf, feb13.pdf, feb16.pdf, feb19.pdf, feb20.pdf, feb22.pdf, feb23.pdf, mar6.pdf, mar8.pdf, mar9.pdf, mar15.pdf, mar16.pdf, mar20.pdf mar22_23.pdf apr3.pdf apr5.pdf apr10.pdf apr12.pdf apr13.pdf apr17.pdf apr19.pdf apr20.pdf apr24.pdf apr26.pdf apr27.pdf apr28.pdf

    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 and MATLAB are the default programming languages for the course. You should use one of these two languages 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 "2013anz7535" and your name is "Nilesh Pathak", your submission directory should be named as "2013anz7535_nilesh_pathak". 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.

    Practice Questions


    1. Assignment 3. Due Date: Sunday April 8, 2018. 11:50 pm.
    2. Assignment 2
      Datasets: Check out the links in the Assigment text.
      Due Date: Sunday Mar 11, 2018. 11:50 pm.
    3. Assignment 1
      Due Date: Monday February 12, 2018. 11:50 pm.

    Grading Policy (Tentative)

    Assignment 1 7%
    Assignment 2 8%
    Assignment 3 9%
    Assignment 4 10%
    Minor I 15%
    Minor II 20%
    Major 31%