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COL774: Machine Learning
General Information
Semester: Sem I, 2023-24.
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
Venue: LHC 418
Sign up for Piazza
Code: As (will be) announced in class.
Sign up for Enrolling in the Course (If not already enrolled): (vacancy has been increased!)Click here.
Notes:
- 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.
TA Assignment:Click here
Announcements
[Oct 21, 2023] Assignment 3 (Both Parts) is out! Due Date (for both parts): Tuesday October 31st, 11:50 pm.
[Oct 8, 2023] Assignment 3 (Part I) is out! Due Date (for both parts): Sunday October 29th, 11:50 pm.
[Sep 26, 2023] Assignment 2 (Complete) is out! Due Date: Wednesday Oct 4, 11:50 pm.
[Sep 6, 2023] Assignment 2 (Part I) is out! Due Date: Wednesday Oct 4, 11:50 pm.
[Aug 25, 2023] TA assignment has beein posted on the website!
[Aug 13, 2023] Assignment 1 is out! Due Date: Friday Sep 1,11:50 pm.
[July 25, 2023]: Course Website has been updated!
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) |
Class Notes/Other Resources |
1 | Introduction | |
July 25, July 26, July 28 |
2 | Supervised Learning Basics - Linear Regression, Gradient Descent |
lin-log-reg.pdf
| Aug 1, Aug 2, Aug 4 |
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3 | Gradient Descent (Including Convergence Properties), Stochastic Gradient Descent |
lin-log-reg.pdf
| Aug 8, Aug 9, Aug 11,
Aug 16, Aug 18 |
|
4 | Linear Regression - alternate intepretation (probabilistic), Logistic Regression, GLMs |
lin-log-reg.pdf
|
Aug 19, Aug 22, Aug 23 |
|
5 | Gaussian Discriminant Analysis (GDA) |
gda_nb.pdf |
Aug 25, Aug 29, Aug 30
| |
6 | Naive Bayes |
gda_nb.pdf
| Sep 1, Sep 5, Sep 6,
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7,8 | Support Vector Machines |
svm.pdf
| Sep 19, Sep 20,
Sep 22,
Sep 26, Sep 29
| |
9 | Decision Trees, Random Forests |
Mitchell, Chapter 3.
dtrees.pdf.
Online Resources:
Random Forests,
Gradient Boosting - Wikipedia, |
Oct 10, Oct 11
Oct 13, Oct 14
Paper by Friedman (2001) (up to Section 4.5)
|
10 | Neural Networks |
Mitchell, Chapter 4.
nnets.pdf
nnets-hw.pdf
|
Oct 17, Oct 18
Oct 20
| 11 | Deep Learning |
cnn.pdf
Online Resource:
Convolutional Neural Networks |
Oct 25, Oct 27,
Oct 28-slides, Oct 28-notes
|
12 | K-Means, Gaussian Mixture Models |
kmeans.pdf
gmm.pdf
|
Oct 31, Nov 1,
Nov 3
|
13 | Expectation Maximiation (EM), Principal Component Analysis (PCA) |
em.pdf
pca.pdf |
Nov 7, Nov 8,
Nov 10
|
14 | Learning Theory, Model Selection |
Mitchell, Chapter 7.
theory.pdf
model.pdf |
Nov 14, Nov 15,
Nov 17
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For week-wise notes, see the Content Table Above.
Video Lectures:
Aug 30,
Sep 1,
Sep 5,
Sep 6,
Sep 19,
Sep 20,
Sep 22
Sep 26,
Sep 29,
Oct 10,
Oct 11,
Oct 13,
Oct 14,
Oct 17,
Oct 18,
Oct 20,
Oct 25,
Oct 27,
Oct 28 (Part a),
Oct 28 (Part b),
Oct 31,
Nov 1,
Nov 3,
Nov 7,
Nov 8,
Nov 10,
Nov 14,
Nov 15,
Nov 17
Video Lectures from Previous offering can be acccessed here
COL 774, Sem I, 2021-22 Course Page (Search for Videos)
References (latest)
References (older)
- Machine Learning.The Art and Science of Algorithms that Make Sense of Data
Peter Flach, Cambridge University Press, 2012.
- 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 "2021cs19535" and your name is "Nitika Rao", your
submission directory should be named as "2021cs19535_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).
Practice Questions
Assignments
- Assignment 3
Starter Code and Dataset. Part A [Updated: see Piazza post for details].
Starter Code and Dataset. Part B
Due Date (both parts) [Updated]: Tuesday Ocotber 31st. 11:50 pm.
- Assignment 2 [Updated! Sep 26th, 2023]
Datasets. Part 1,Part 2 (linked from pdf)
Due Date: Wednesday October 4, 2023. 11:50 pm
- Assignment 1
Datasets: ass1_data.zip
Due Date: Friday September 1, 2023. 11:50 pm
Grading Policy (Tentative)
Assignments (4) | Ass1: 7%. Ass2: 9%, Ass3: 9%, Ass4: 10 %. [Total Assignment Weight: 35%] |
Minor | 25% |
Major | 40% |
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