COL774: Machine Learning
Semester: Sem II, 2018-19.
Instructor: Parag Singla (email: parags AT cse.iitd.ac.in)
Class Timings (Slot F):
Venue: LHC 527.
- Tue, 11:00 am - 11:50am
- Thu, 11:00 am - 11:50am
- Fri, 11:00 am - 11:50am
Teaching Assistants (TAs)
Sign up for Piazza
Code: As sent over email.
[Apr 18, 2019]: Notes for the last class are also up. Check out the last two files in the notes section!
[Apr 26, 2019]: Class notes (until Apr 25) and last two years' major question papers are up now!
[Mar 28, 2019]: Updated Assignment 3 is now uploaded!
[Mar 15, 2019]: Assignment 3 is out!
[Mar 3, 2019]: Assignment 2 (second question) is out!
[Feb 15, 2019]: Assignment 2 (first question) is out!
[Feb 1, 2019]: Last year's Minor 1 exam is up for practice.
[Feb 1, 2019]: Class notes up to Feb 1 are up.
[Jan 22, 2019]: Assignment 1 is out!
[Jan 22, 2019]: Class notes up to Jan 18 are up.
[Jan 14, 2019]
Sign-up for the course page on Piazza (code as sent over email).
[Jan 14, 2019]: Course website is finally up!
NOTE: The exact list of topics below is still tentative (until we are past that week). We will update it as we go through the lectures in each week. So, stay tuned!
|1 || Introduction || ||
|2,3 || Linear Regression (and Its Variants)
|| Bishop, Chapter 3.1
|4 || Logistic Regression, Generalized Linear Models
|| Bishop, Chapter 3.1
|5 || Gaussian Discriminant Analysis (GDA), Naive Bayes
||Bishop, Chapter 4
|6,7 || Support Vector Machines
|| Bishop, Chapter 7.1
|8 || Decision Trees, Random Forests
|| Mitchell, Chapter 3.
|9 || Neural Networks
|| Mitchell, Chapter 4
|10 || Deep Learning
Convolutional Neural Networks
|11 || K-Means, Gaussian Mixture Models
|12 ||Expectation Maximiation (EM), Principal Component Analysis (PCA)
|13 || Learning Theory, Model Selection
|| Mitchell, Chapter 7
|14 || Advanced Topics
- 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 assignment problems with other students in the class. But all your code should be
produced independently without looking at/referring to anyone else's code.
- Python (preferred) and
MATLAB are the default programming
languages for the course. You should use one of these two languages for programming your
assignments unless otherwise explicitly allowed.
- Code should be submitted using Moodle Page.
Make sure to include commenrs for readability.
- Create a separate directory
for each of the questions named by the question number. For instance, for question 1,
all your submissions files 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 "2016anz7535" and your name is "Nitika Rao", your
submission directory should be named as "2016anz7535_nitika_rao". You should zip your
directory and name the resulting file as "yourentrynumber_firstname_lastname.zip" e.g. in
the above example it will be "2016anz7535_nitika_rao.zip". This single zip file should
be submitted online.
- Honor Code: Any cases of copying will be awarded a zero on the assignment and a
penalty of -10. More severe penalties may follow.
- Late Policy: You are allowed a total of 5 late days acorss all the assignments. You are
free to decide how you would like to use them. You will get a zero on an assignment once you exceed the
(total) allowed exemption of 5 days.
- Assignment 3
Due Date: Friday April 5, 2019. 11:50 pm.
- Assignment 2 (Part A)
Dataset (Naive Bayes): ass2_data.zip
Assignment 2 (Part B)
Due Date (for both parts):
March 12 March 13, 2019. 11:50 pm.
- Assignment 1
Due Date: Tuesday February 12, 2019. 11:50 pm.
|Assignments (1-4) || 30-35% (total)
|Minors (1,2) || 30-35% (total)
|Major || 30-35%