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COL774: Machine Learning
General Information
Semester: Sem I, 2020-21.
Instructor: Parag Singla (email: parags AT cse.iitd.ac.in)
Class Timings (Slot C):
- Tue, 8:00 am - 8:50am
- Wed, 8:00 am - 8:50am
- Fri, 8:00 am - 8:50am
- Sat, 8:00 am - 8:50am (As per academic schedule)
Venue: Online:
Click Here
TA Assignment Details:
Click here
Sign up for Piazza
Code: As announced over email.
Announcements
[Dec. 25, 2020]: Assignment 4 is out!
[Dec. 15, 2020]: Dec 14 - Dec 20 lectures are up on the website now!
[Dec. 6, 2020]: Assignment 3 has been updated. Due date: Tuesday Dec 22, 2020, 11:59 pm.
[Dec. 4, 2020]: Assignment 3 is out. Due date: Tuesday Dec 22, 2020, 11:59 pm.
[Nov. 14, 2020]: Assignment 2 is out. Due date: Wednesday Dec 2, 2020, 11:50 pm.
[Oct. 19, 2020]: Assignment 1 is out. Due date: Friday Nov 6, 2020, 11:50 pm.
[Sep. 29, 2020]: Due to a large demand, the course is open for CSE/SIT students only. Others are welcome to sit-through the course.
[Sep. 28, 2020]: Welcome! COL 774 classes will start from Tuesday Oct 6th.
Course Objectives
(a) To familiarize with/develop the understanding of fundamental concepts of Machine
Learning (ML)
(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
data/problems
(d) To update with some of the latest advances in the field
Course Content
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!
Week |
Topic |
Supplementary Notes (by Andrew Ng and Others) |
Book Chapters |
1 | Introduction | | |
2,3 | Linear Regression (and Its Variants) |
lin-log-reg.pdf
| Bishop, Chapter 3.1 |
4 | Logistic Regression, Generalized Linear Models |
lin-log-reg.pdf
| Bishop, Chapter 3.1 |
5 | Gaussian Discriminant Analysis (GDA), Naive Bayes |
gda_nb.pdf |
Bishop, Chapter 4 |
6,7 | Support Vector Machines |
svm.pdf |
Bishop, Chapter 7.1 |
8 | Decision Trees, Random Forests |
dtrees.pdf
| Mitchell, Chapter 3.
Online Resource:
Random Forests |
9 | Neural Networks |
nnets.pdf
nnets-hw.pdf
|
Mitchell, Chapter 4 |
10 | Deep Learning |
deep_learning_slides.pdf
cnn.pdf
|
Online Resource:
Convolutional Neural Networks |
11 | K-Means, Gaussian Mixture Models |
kmeans.pdf
gmm.pdf
em.pdf
pca.pdf |
|
12 | Expectation Maximiation (EM), Principal Component Analysis (PCA) |
kmeans.pdf
gmm.pdf
em.pdf
pca.pdf |
|
13 | Learning Theory, Model Selection |
theory.pdf
model.pdf |
Mitchell, Chapter 7 |
14 | Advanced Topics |
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Notes: Oct 6, Oct 7,
Oct 9, Oct 10
Oct 13, Oct 14,
Oct 16,Oct 17,
Oct 20,Oct 21,
Oct 23,Oct 24,
Oct 27,Oct 28,
Oct 30,Oct 31,
Nov 4,Nov 6,
Nov 7,Nov 13,
Nov 17, Nov 18,
Nov 20,Nov 21,
Nov 24,Nov 25,
Nov 27,Nov 29,
Self Study - Neural Networks (1),
Self Study - Neural Networks (2),
Dec 15,Dec 16,
Dec 18 (a),Dec 18,
Dec 19,
Dec 22,
Dec 23,Dec 26,
Dec 27,
Dec 29,Dec 30,
Jan 1,Jan 2
Videos: Oct 6,
Oct 7,
Oct 9,
Oct 10,
Oct 13,
Oct 14,
Oct 16,
Oct 17,
Oct 20,
Oct 21,
Oct 23,
Oct 24,
Oct 27,
Oct 28,
Oct 30,
Oct 31,
Nov 4,
Nov 6,
Nov 7,
Nov 13,
Nov 17,
Nov 18,
Nov 20,
Nov 21,
Nov 24,
Nov 25,
Nov 27,
Nov 29,
Self Study - Neural Networks(1) ,
Self Study - Neural Networks (2)
Dec 15,
Dec 16,
Dec 18 (a) [watch 28:50 minutes onward],
Dec 18,
Dec 19,
Dec 22,
Dec 23,
Dec 26,
Dec 27,
Dec 29,
Dec 30,
Jan 1,
Jan 2
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 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 "2017anz7535" and your name is "Nitika Rao", your
submission directory should be named as "2017anz7535_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 "2017anz7535_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 (buffer) days acorss all the assignments. You are
free to decide how you would like to use them.
You will get a peanlty of 10% deduction in marks (per day) for every additional late day in submission
used beyond the allowed 5 buffer days.
This applies to the first 3
assignments in the course
(i.e., leaving out the last assigment based on a competition).
Practice Questions
Assignments
- Assignment 4
Due Date: Sunday Jan 17, 2021, 11:59 pm
- Assignment 3 [Updated: Dec 6, 2020 @ 10:40 pm]
Datasets:
Part (1): Decision Tree Data
Part (2): Kannda Digits Data,
MNIST Data [MNIST data provided only for extra fun - no Credits!]
Due Date: Tuesday December 22, 2020, 11:59 pm
- Assignment 2
Datasets: Yelp ,
FMNIST
Due Date: Wednesday December 2, 2020, 11:50 pm
- Assignment 1
Datasets: ass1_data.zip
Due Date:Friday November 6, 2020, 11:50 pm
Grading Policy (Tentative)
Assignments | Ass1: 7%, Ass2: 9%, Ass3: 9%, Ass4: 10% |
| [Total Assignment Weight: 35%] |
Minor | 25% |
Major | 40% |
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