CSL865: Special Topics in Computer Applications: Machine Learning

General Information

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

Class Timings (Slot F):
  • Tuesday, 11:00am - 11:55am
  • Thursday, 11:00am - 11:55am
  • Friday, 11:00am - 11:55am
Venue:IV LT 2

Teaching Assistant: Yamuna Prasad (email: csz098192 AT cse.iitd.ac.in)


  • [Wed Oct 31]: Assignment 3 is out! Due Date: Friday Nov 16 (in class).
  • [Thu Oct 11]: No Class on Friday Oct 12. Extra class on Monday Oct 15. 2pm - 3pm. Venue: Bharti 201.
  • [Sun Sep 30]: Extra class on Monday Oct 1. 2pm - 3pm. Venue: Bharti 204.
  • [Sat Sep 22]: Assignment 2 is out! Due Date: Tuesday October 16 (in class)
  • [Tue Sep 11]: Assignment 1 is now due on Friday September 14 (in class).
  • [Wed Aug 20]: Assignment 1 is out! Due Date: Tuesday September 11 (in class).
  • [Wed Aug 7]: After all the confusion, we seem to have settled for IV LT 2 as our permanent venue for the class.
  • [Mon Aug 6]: Permanent venue for the class will be Block III, Room 356.
  • [Mon Jul 30]: Venue for tomorrow's (Tue Jul 31) class is IV LT 2.
  • [Thu Jul 26]: On Fri July 27, we will plan on meeting for additional half an hour after the regular slot i.e. the class time would be 11:00 am - 12:30 pm.

Course Content

WeekTopic Book ChaptersSupplementary Notes
1 Introduction Duda, Chapter 1
2,3 Linear and Logistic Regression, Gaussian Discrimnant 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
7 Decision Trees Mitchell, Chapter 3 dtrees.pdf
8,9 Naive Bayes, Bayes Classifier, Bayesian Networks, Markov Networks Mitchell, Chapter 6 nb.pdf, bayes.pdf Conjugate Prior mn.pdf
10,11 Learning Theory, Model Selection Mitchell, Chapter 7 theory.pdf model.pdf
12 K-Means, Gaussian Mixture Models, EM kmeans.pdf gmm.pdf em.pdf
13 PCA and ICA pca.pdf ica.pdf
14 Revision

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 Instrutions

  1. You are free to discuss the problems with other students in the class. You should include the names of the people you had discussion with in your submission.
  2. All your solutions should be produced independently without refering to any discussion notes.
  3. All the non-programming solutions should be submitted using a hard copy. If you are writing by hand, write legibly.
  4. All the programming should be done in MATLAB. Include comments for readability.
  5. Required code should be submitted using Moodle Page.
  6. You should archive all your submission (code) in one single zip file. This zip file should be named as "yourentrynumber_firstname_lastname.zip". For example, if your entry number is "2008anz7535" and your name is "Nilesh Pathak", your submission should be named as "2008anz7535_nilesh_pathak.zip
  7. Honor Code: Any cases of copying will be awarded a zero on the assginment. More severe penalties may follow.
  8. Late Policy: You will lose 20% for each late day in submission. Maximum of 2 days late submissions are allowed.


  1. Assignment 3. Due Date: Friday November 16 (in class). Coding problem due on Saturday Nov 24.
  2. Assignment 2. Due Date: Tuesday October 16 (in class).
  3. Assignment 1. Due Date: Tuesday September 11 (in class). Friday September 14 (in class).

Grading Policy

Assignments (3) 24%
Class Participation 6%
Minor I 15%
Minor II 15%
Major 40%