CSL341: Fundamentals of Machine Learning

General Information

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

Class Timings (Slot B):
  • Monday, 9:30am - 10:55am
  • Thursday, 9:30am - 10:55am
Venue:WS 101 (Workshop Room 101) Bharti 101

Teaching Assistants

Name Email
Abhinav Kumar cs5090231 AT cse.iitd.ac.in
Anuj Gupta agupta AT cse.iitd.ac.in
Arpit Jain cs5090236 AT cse.iitd.ac.in
Happy Mittal csz138233 AT cse.iitd.ac.in
Shubham Gupta cs5090252 AT cse.iitd.ac.in
Sudhanshu Sekhar cs5090255 AT cse.iitd.ac.in
Yamuna Prasad yprasad AT cse.iitd.ac.in


  • [Thu Oct 31]: Assignment 2, New Due Date: Monday Nov 4 (11:50 pm).
  • [Mon Sep 30]: Assignment 2 is out! Due Date: Thursday Oct 31 (11:50 pm).
  • [Fri Sep 27]: Assignment submission instructions have been updated (See below).
  • [Wed Sep 25]: Assignment 1 has been updated. New Due Date: Sunday Sep 29 (11:50 pm).
  • [Wed Sep 4]: The venue for the class on Thursday Sep 5 will be Bharti 101 (instead of WS 101).
  • [Sat Aug 10]: Assignment 1 is out! Due Date: Sunday Sep 15 (11:50 pm).
  • [Wed Jul 31]: The course website is up, finally!

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 ML to CrowdSourcing and NLP crowd-ml.pdf nlp-ml.pdf

Additional Reading

Review Material

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


  1. Pattern Recognition and Machine Learning. Christopher Bishop. First Edition, Springer, 2006.
  2. Pattern Classification. Richard Duda, Peter Hart and David Stock. Second Edition, Wiley-Interscience, 2000.
  3. 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 Sakai Page.
  5. [Updated October 31, 2013]: 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 2 New Due Date: 11:50 pm, Monday November 4, 2013.
  2. Assignment 1. New Due Date: Sunday September 29, 2013.


To know more about the class project, click here.

Grading Policy

Assignments (2) 16%
Project 25%
Minor I 12%
Minor II 12%
Major 35%