CSL341: Fundamentals of Machine Learning

General Information

Instructor: Parag Singla (email: parags AT cse.iitd.ac.in)

Class Timings (Slot H):
  • Monday, 11:00am - 11:50am
  • Wednesday, 11:00am - 11:50am
  • Thursday, 12:00 noon - 12:50pm
Venue: Bharti 201.

Teaching Assistants (TAs)
TA Assignment


  • [Nov 8, 2014]: Assignment 3 has been updated! Due Date: Same as beofe.
  • [Oct 25, 2014]: Assignment 3 is out! Due Date: Sunday November 16, 11:50 pm.
  • [Sep 14, 2014]: Assignment 2 is out! Due Date: Tuesday October 7, 11:50 am.
  • [Sep 10, 2014]: Assignment 1 is now due on Thursday September 11, 11:50 pm.
  • [Aug 18, 2014]: Assignment 1 is out! Due Date: Sunday September 7, 11:50 pm.
  • [Aug 6, 2014]: Important! Extra Classes on Wednesday Aug 13 (12 noon - 1:30 pm) and Thursday Aug 21 (3pm - 4:30pm.). Venue: Bharti 501.
  • [Aug 3, 2014]: Classes will resume on Monday Aug 4. 11 am - 12 noon

    Course Content

    WeekTopic Book ChaptersSupplementary Notes
    1 Introduction Duda, Chapter 1
    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 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 Application of Machine Learning

    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. 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 (code/graphs/write-up) 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 "2009anz7535" and your name is "Nilesh Pathak", your submission directory should be named as "2009anz7535_nilesh_pathak". You should zip your directory and name the resulting file as "yourentrynumber_firstname_lastname.zip" e.g. in the above example it will be "2009anz7535_nilesh_pathak.zip". This single zip file should be submitted online.
    6. Honor Code: Any cases of copying will be awarded a zero on the assignment. More severe penalties may follow.
    7. Late Policy: You will lose 20% for each late day in submission. Maximum of 2 days late submissions are allowed.


    1. Assignment 1. Due: Thursday September 11, 2014. 11:50 pm.
    2. Assignment 2. Due: Tuesday October 7, 2014. 11:50 am.
    3. Assignment 3. (Updated: Sat Nov 8). Due: Sunday November 16, 2014. 11:50 pm.

    Grading Policy (Tentative)

    Assignments (3) 28% (Assignments 1,2 - 8% each, Assignment 3 - 12 %)
    Quiz (1) 6%
    Minor I 15%
    Minor II 15%
    Major 36%