MACHINE INTELLIGENCE & LEARNING
Sem-I, 2024

Updates

New Assignment released: [Assignment #2 - Decision Trees]
New Assignment released: [Assignment #1 - Linear Regression]
New Lecture is up: Transformer (Conclusion) [Slides-I] [Slides-II]
New Lecture is up: RNN (conclusion), Transformer [RNN Slides] [LSTM Slides] [Attention Slides]
New Lecture is up: CNN, Intro to RNN [CNN Slides] [RNN Slides] [CNN Reading]
New Lecture is up: Perceptron and Multi-layer Perceptron [reading-I]
Dr. Sumit Bhatia, Senior Machine Learning Scientist at Adobe, delivered a guest lecture on “Machine Learning, Everywhere!”.

Course Description

This is an introductory course on Machine Learning (ML) that is offered to undergraduate students. The contents are designed to cover both theoretical and practical aspects of several well-established ML techniques. The assignments will contain theory and programming questions that help strengthen the theoretical foundations as well as learn how to engineer ML solutions to work on simulated and publicly available real datasets. The project(s) will require students to develop a complete Machine Learning solution requiring preprocessing, design of the classifier/regressor, training and validation, testing and evaluation with quantitative performance comparisons.

Prerequisites:

Basic computer science principles (Big-O notation, Comfortably write non-trivial code in Python/numpy)
Probability (Random Variables, Expectations, Distributions)
Linear Algebra & Multivariate/Matrix Calculus (Gradients and Hessians, Eigenvalue/vector)

TA Availability

Name	Time	Contact
Vaibhav	Mon@4-5	mt1210236@maths.iitd.ac.in
Soumyo	Tue@4-5	aiy237526@scai.iitd.ac.in
Sahil	Wed@4-5	sahil.mishra@ee.iitd.ac.in
Aswini	Wed@3-4	eez238359@ee.iitd.ac.in
Palash	Thu@4-5	eez228472@ee.iitd.ac.in
Anant	Fri@3-4	aib232068@scai.iitd.ac.in