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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
Announcements
[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
Week | Topic | Book Chapters | Supplementary 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
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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
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13 | Learning Theory, Model Selection | Mitchell, Chapter 7 |
theory.pdf
model.pdf |
14 | Application of Machine Learning | |
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Review Material
References
- Machine Learning: A Probabilistic Perspective.
Kevin Murphy. MIT Press, 2012.
- Pattern Recognition and Machine Learning. Christopher Bishop. First Edition, Springer, 2006.
- Pattern Classification. Richard Duda, Peter Hart and David Stock. Second Edition, Wiley-Interscience, 2000.
- Machine Learning. Tom Mitchell. First Edition, McGraw-Hill, 1997.
Assignment Submission Instructions
- 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.
- All your solutions should be produced independently without referring to any
discussion notes or the code someone else would have written.
- All the programming should be done in MATLAB.
Include comments for readability.
- Code should be submitted using Moodle Page.
- 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.
- Honor Code: Any cases of copying will be awarded a zero on the assignment. 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 1. Due: Thursday September 11, 2014. 11:50 pm.
Datasets:
- Assignment 2. Due: Tuesday October 7, 2014. 11:50 am.
Datasets:
- Assignment 3. (Updated: Sat Nov 8). Due: Sunday November 16, 2014. 11:50 pm.
Datasets:
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% |
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