
CSL865: Special Topics in Computer Applications: Machine Learning
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.
Week  Topic  Book Chapters  Supplementary Notes 
1  Introduction  Duda, Chapter 1  
2,3  Linear and Logistic Regression, Gaussian Discrimnant Analysis  Bishop, Chapter 3.1, 4 
linlogreg.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  KMeans, Gaussian Mixture Models, EM  
kmeans.pdf
gmm.pdf
em.pdf 
13  PCA and ICA  
pca.pdf
ica.pdf 
14  Revision   
Review Material
References
 Pattern Recognition and Machine Learning. Christopher Bishop. First Edition, Springer, 2006.
 Pattern Classification. Richard Duda, Peter Hart and David Stock. Second Edition, WileyInterscience, 2000.
 Machine Learning. Tom Mitchell. First Edition, McGrawHill, 1997.
Assignment Submission Instrutions
 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.
 All your solutions should be produced independently without refering to any discussion notes.
 All the nonprogramming solutions should be submitted using a hard copy. If you are writing
by hand, write legibly.
 All the programming should be done in MATLAB.
Include comments for readability.
 Required code should be submitted using Moodle Page.
 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
 Honor Code: Any cases of copying will be awarded a zero on the assginment. More severe penalties may follow.
 Late Policy: You will lose 20% for each late day in submission. Maximum of 2 days late submissions are allowed.
Assignments
 Assignment 3. Due Date: Friday November 16 (in class). Coding problem due on Saturday Nov 24.
 Assignment 2. Due Date: Tuesday October 16 (in class).
 Assignment 1. Due Date:
Tuesday September 11 (in class). Friday September 14 (in class).
Datasets
Grading Policy
Assignments (3)  24% 
Class Participation  6% 
Minor I  15% 
Minor II  15% 
Major  40% 
