COL774: Machine Learning

General Information

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 526.

Teaching Assistants (TAs)
TA Assignment (Updated!)


  • [Apr 30, 2017]: A new set of practice questions has been posted (see below)!
  • [Apr 7, 2017]: Assignment 4 is out! Due Date: Friday April 21, 11:50 pm.
  • [Mar 17, 2017]: TA Assignment has been updated!
  • [Mar 14, 2017]: Assignment 3 is out! Due Date: Tuesday April 4, 11:50 pm.
  • [Feb 27, 2017]: Remaining Portion of Assignment 2 is out (see below). Due Date: Friday March 10, 11:50 pm.
  • [Feb 12, 2017]: Assignment 2 is out (see below). Due Date: Friday March 10, 11:50 pm.
  • [Jan 26, 2017]: A set of practice questions has been posted for Minor 1 (see below).
  • [Jan 26, 2017]: Assignment 1 deadline is now Friday February 10, 11:50 pm.
  • [Jan 21, 2017]: Assignment 1 is out! It is due on Wednesday February 8, 11:50 pm.

    Course Content

    WeekTopic Book ChaptersSupplementary Notes
    1 Introduction
    2,3 Linear and Logistic Regression, Gaussian Discriminant Analysis Bishop, Chapter 3.1, 4 lin-log-reg.pdf, gda.pdf
    4,5 Support Vector Machines Bishop, Chapter 7.1 svm.pdf
    6 Neural Networks Mitchell, Chapter 4 nnets.pdf nnets-hw.pdf
    7 Decision Trees Mitchell, Chapter 3 dtrees.pdf
    8,9 Naive Bayes, Bayesian Statistics Mitchell, Chapter 6 nb.pdf, bayes.pdf Conjugate Prior
    map.pdf model.pdf
    10,11 K-Means, Gaussian Mixture Models, EM kmeans.pdf gmm.pdf em.pdf
    12 PCA pca.pdf
    13 Learning Theory, Model Selection Mitchell, Chapter 7 theory.pdf model.pdf
    14 Advanced Topics: Deep Learning
    Online Resource:
    Convolutional Neural Networks

    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 problems with other students in the class. You should include the names of the people you had a significant discussion with in your submission.
    2. All your solutions should be produced independently without referring to any discussion notes or the code someone else would have written.
    3. All the programming should be done in MATLAB (unless otherwise allowed explicitly). Include comments for readability.
    4. Code should be submitted using Moodle Page.
    5. 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.
    6. Honor Code: Any cases of copying will be awarded a zero on the assignment and a penalty of -10. More severe penalties may follow.
    7. Late Policy: You are allowed a total of 5 late days 6 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 6 days.

    Practice Questions


    1. Assignment 4 [Updated: 11:40 pm, April 13] . Due Date: Friday April 21, 2017. 11:50 pm.

    Grading Policy (Tentative)

    Assignment 1 7%
    Assignment 2 7%
    Assignment 3 7%
    Assignment 4 10% (5 for Part (a) + 5 for Competitive Question)
    Quiz (May be) 0-4%
    Minor I 15%
    Minor II 15%
    Major 36-40%