COL774: Machine Learning
Semester: Sem I, 2022-23.
Instructor: Parag Singla (email: parags AT cse.iitd.ac.in)
Class Timings (Slot C):
Venue: LHC 316
- Tue, 8:00 am - 8:50am
- Wed, 8:00 am - 8:50am
- Fri, 8:00 am - 8:50am
Sign up for Piazza
Code: As (will be) announced in class.
Sign up for Enrolling in the Course (If not already enrolled): Click here
- Only students from CSE/EE/Maths/SIT/ScAI should fill this (unless I have confirmed an exception over email).
- Students who are only planning to sit through this course Should NOT fill this form.
[Oct 28, 2022]: Assignment 4 is out. Due Date: Monday, November 28, 2022, 11:50 pm.
[Oct 16, 2022]: Make-up class held on Saturday Oct 16, 2022. Recorded lecture is available from course contents below (topic #10).
[Oct 6, 2022]: Assignment 3 is out. Due Date: Wednesday, October 26, 2022, 11:50 pm.
[Sep 10, 2022]: Assignment 2 is out. Due Date: Tuesday, October 4, 2022, 11:50 pm.
[Aug 27, 2022]: Make-up class held on Saturday Aug 27, 2022. Recorded lecture is available from course contents below (topic #4).
[Aug 23, 2022]: Assignment 1 is out. Due Date: Friday, September 9, 2022, 11:50 pm.
[Aug 4, 2022]: Welcome! First Class of COL 774 will be on Friday Aug 5. 8 am - 9 am.
(a) To familiarize with/develop the understanding of fundamental concepts of Machine
(b) To develop the understanding of working of a variety of ML algorithms (both
supervised as well as unsupervised)
(c) To learn to apply ML algorithms to real world
(d) To update with some of the latest advances in the field
NOTE: The exact list of topics below is tentative (until we are past that week).
We will update it as we go through the lectures in each week. So, stay tuned!
Aug 5, Aug 6,Aug 10,
Aug 12, Aug 16, Aug 17
Aug 23, Aug 24, Aug 26,
Aug 27, Aug 30, Aug 31,
Sep 2, Sep 6, Sep 7,
Sep 7, Sep 9, Sep 13,
Sep 14, Sep 16, Sep 17,
Sep 20, Sep 21,Sep 23,
Oct 7, Oct 8, Oct 11,
Oct 12, Oct 15, Oct 18,
[Oct 19: slides, hand-written],
Oct 21, Oct 22,Oct 25,
Oct 26,Oct 28, Nov 1,
Nov 2,Nov 4,Nov 9,
Nov 11,Nov 12
(by Andrew Ng and Others)
|1 || Introduction || ||
|2,3 || Linear Regression (and Its Variants)
|| Bishop, Chapter 3.1
|4 || Logistic Regression, Generalized Linear Models
Lecture Video: Saturday, Aug 27
| Bishop, Chapter 3.1
|5 || Gaussian Discriminant Analysis (GDA), Naive Bayes
||Bishop, Chapter 4
|6,7 || Support Vector Machines
Lecture Video: Saturday, Sep 17
| Bishop, Chapter 7.1
|8 || Decision Trees, Random Forests
|| Mitchell, Chapter 3.
Gradient Boosting - Wikipedia,
Paper by Friedman (2001) (up to Section 4.5)
|9 || Neural Networks
|| Mitchell, Chapter 4
|10 || Deep Learning
deep_learning_slides.pdf (GANs, Dropouts)
Lecture Video: Saturday Oct 15
Lecture Video: Saturday Oct 22
Additional/Self Reading (Watch 31:15 onward)
| Online Resource:
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
Video Lectures from Previous offering can be acccessed here
COL 774, Sem I, 2021-22 Course Page (Search for Videos)
- 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 is the default programming
languages for the course. You should use it 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 "2020cs19535" and your name is "Nitika Rao", your
submission directory should be named as "2020cs19535_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 "2021cs19535_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 an additional penalty equal to the negative of the total
weightage of the assignment. More severe penalties may follow.
- Late Policy: You are allowed a total of 5 late (buffer) days acorss the first 3 assignments. You are free to decide how you would like to use them. The late policy (if any) for
the last assignment will be announced separately.
You will get a penalty of 10% deduction in marks (per day) for every additional late day in submission used beyond the allowed 5 buffer days (applicable to first 3 assignments only).
- Assignment 4
Due Date: Monday November 28, 2022, 11:50 pm.
- Assignment 3 [Updated: Friday Oct 14, 8:15 AM]
Datasets: Available from the links given in the assignment pdf
Due Date: Wednesday October 26, 2022. 11:50 pm
- Assignment 2 [Updated: Sunday Sep 18, 9:00 AM].
Datasets: Available from the link given in the assignment pdf
Due Date:Tuesday October 4, 2022. 11:50 pm
- Assignment 1
Due Date:Friday September 9, 2022. 11:50 pm
|Assignments (4) || Ass1: 7%. Ass2: 9%, Ass3: 9%, Ass4: 10 %. [Total Assignment Weight: 35%]
|Minor || 25%
|Major || 40%