COL774: Machine Learning

Course Objectives

Class Notes/Videos (Date-Wise):

Additional Resources

• Proof of Convergence of Gradient Descent and Stochastic Gradient Descent. By Raghu Meka.
• Stochastic Gradient Descent: Algorithm , Understanding it better (Youtube Videos by Andrew Ng)
• On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. Andrew Y. Ng and Michael Jordan. NIPS 2001.
• Generative and Discrminative Classifiers: Naive Bayes and Logistic Regression. Chapter 1, Machine Learning. Tom Mitchell.
• Generative and Discrminative Classifiers: Naive Bayes and Logistic Regression. Chapter 1, Machine Learning. Tom Mitchell.
• Notes on Avoiding Overfitting in Decision Trees [This is on internal network. For students who need to access from outside IIT Delhi, you find this in resources section on Piazza.]
• Deep Learning Book
  

Additional Reading (Papers)

• A Unified Bias-Variance Decomposition by Pedro Domingos.
• Neural Machine Translation by Jointly Learning to Align and Translate by Bahdanau et al.
• Attention is All You Need by Vaswani et al.

Review Material

Topic	Notes
Probability	prob.pdf
Linear Algebra	linalg.pdf
Gaussian Distribution	gaussians.pdf
Convex Optimization (1)	convex-1.pdf

References (latest)

• New! Probabilisitic Machine Learning: An Introduction/Advanced Topics, Kevin Murphy. MIT Press, 2022/2023. (see the introductory one for this class)
• New! An Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (and Jonathan Taylor -- for Python version), Springer 2021/2023.

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.

1. 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.
2. Python is the default programming languages for the course. You should use it for programming your assignments unless otherwise explicitly allowed.
3. Code should be submitted using Moodle Page. Make sure to include commenrs for readability.
4. 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 "2022cs19535" and your name is "Nitika Rao", your submission directory should be named as "2022cs19535_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 "2022cs19535_nitika_rao.zip". This single zip file should be submitted online.
5. 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.
6. 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).
7. Audit Policy: To get an Audit Pass in the course, you are required to get a score equivalent to getting a C grade (or more) in the course.

• Major (2024-25, Sem II offering)
• Minor (2024-25, Sem II offering)
• Major (2023-24, Sem I offering)
• Minor (2023-24, Sem I offering)
• Major (2022-23, Sem I offering)
• Minor (2022-23, Sem I offering)
• Major (2021-22, Sem I offering)
• Minor (2021-22, Sem I offering)
• Major (2020-21, Sem I offering)
• Minor (2020-21, Sem I offering)
• Minor 2 (2019 offering)
• Major (2019 offering)
• Minor 1 (2018 offering)
• Minor 2 (2018 offering)
• Major (2018 offering)
• Minor 1 (2017 offering)
• Minor 2 (2017 offering)
• Major (2017 offering)
• Pratice Questions for Minor 1 (from 2017)
• Pratice Questions for Minor 2 (from 2017)
• Pratice Questions for Major (from 2017)

1. Assignment 4 Due Date: Tuesday Nov 25, 2025. 11:59 pm.
2. Assignment 3 (Both Parts). Updated: Sunday October 19, 2025.
   New Due Date (for both parts): Friday October 31, 2025. 11:59 pm.
3. Assignment 2 (with both parts)
   Due Date (for both parts): Wednesday October 8, 2025. 11:59 pm.
4. Assignment 1.
   [Updated: Aug 31, 2025]. New Due Date: Friday September 5, 2025. 11:59 pm.

Grading Policy (Tentative)