Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery	Frequency Of Classes
English	Self Study, Virtual Classroom	Video and Text Based	Weekends

Important dates

Application Date

End Date : 16 Jan, 2026

Course Commencement Date

Start Date : 17 Jan, 2026

End Date : 18 Jul, 2026

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	IIT Delhi

The fees for the course is :

Fees components	Amount
Course fees	Rs. 1,79,000

Instalment	Instalment Date	Amount
Application Fee	To be paid at the time of Application	1,000
1st Instalment	Within 5 days of offer roll-out	45,000
2nd Instalment	16th February, 2026	45,000
3rd Instalment	18th March, 2026	45,000
4th Instalment	17th April, 2026	44,000

The Syllabus

Motivations and Introduction to different ML Paradigm Linear Algebra for ML

Vectors and Matrices
Vector Space and Subspace
System of Linear Equations
The Concept of Rank and Independent Vectors
Inner Product Space
Norms, Positive Definite Matrix
Matrix factorisation (EVD, SVD, QR, LR, etc.)
Projection and Orthogonality

Probability and Statistics for Data science

Random Variables
Distribution and Density Functions
Conditional Probability, Bayes Theorem
Joint Distribution
Concept of Independence, Covariance, and Correlation
Introductory Statistical Inference (Likelihood, MAP, etc.)
Concept of Entropy
Mutual Information, and KL Divergence

Optimisation

Function and Derivatives
Gradient Descent
Stochastic Gradient Descents
Convex Optimisation
Formulation and Optimality Conditions
ADAM Optimiser

Hands-on Demo 1: Linear Algebra using NumPy

Concepts of Linear Algebra and Probability Basics
Optimisation with Practical ML Applications

Simple and Multiple Linear Regression
Hands-on Demo 2: SLR/MLR
Least Squares Approach
Moving Beyond Linearity: Non-linear Regression
Hands-on Demo 3: NLR
Model Selection, Regularisation and Bias-Variance Trade-off
M2 Project: Regression application

Motivation and Introduction to Classification Problems Logistic Regression

Logistic Regression
Hands-on Demo 4: Logistic Regression

Decision Tree

Introduction to Decision Trees
Random Forests, Bagging, and Boosting
Hands-on Demo 5: Random Forests
Interpretability of Machine Learning Models

Hyperplanes

Concept of Hyperplane Classifier

SVM

Support Vector Machines, Kernel SVM
Hands-on Demo 6: SVM
Multi-class Classifiers

Clustering

Clustering Methods
Hands-on Demo 7: Clustering

Project

Classification Application

Neural Networks

Fundamentals of Neural Network and Feedforward Network
Concept of Training and Backpropagation
Hands-on Demo 8: ANN

Convolutional Neural Networks

Fundamentals of Convolution
Convolutional Neural Network Architecture
Hands-on Demo 9: CNN

Recurrent Neural Networks/LSTM

Introduction to Time Series and Sequential Data
Introduction to Language Modelling and NLP
Recurrent Neural Network and LSTM/GRU
Hands-on Demo 10

Graph Neural Networks

Introduction to Graph Data
Graph Neural Network Architecture
Hands-on Demo 11

Transformers

Core mechanics — self-attention, positional encodings, causal mask
Efficiency & fine-tuning — Flash/linear attention, LoRA-FT/adapters
Multimodal extensions — vision-language models

Generative AI

Autoencoder, Variational Autoencoders, Generative Adversarial Networks (GANs)
Diffusion for images and text modalities

LLM Alignment

Alignment pipeline — SFT → reward model → RLHF/DPO/PPO
Alternative approaches — Constitutional AI, RLAIF

Linear Regression Lab

Is there a connection between sales and different types of ad expenditure? In this lab, we try to forecast the sales of a product assuming ad sales are available.

Logistic Regression Lab

Sentiment Analysis of consumers. Can we directly infer the quality of any product based on its reviews?

Decision Tree, Random Forest, XGBoost

In-depth analysis of algorithms on benchmark datasets.

Support Vector Machines

Image classification on fashion MNIST dataset, intuition of soft margin, hard margin, solving SVM using CVXPY

Neural networks

Basic understanding and implementation of each layer of NN. Writing and understanding gradient descent/backpropagation algorithm in Python
Comparison of Neural Networks and SVM on image classification datasets

Convolutional Neural Networks (CNN)

Ever wondered how computers identify faces? We will see how CNN has revolutionized the field of Computer Vision
Understanding layers, visualization of the learning process, Occlusion, GRADCAM

Sequential Model (Recurrent Neural Network/Long Short-Term Memory)

Implementation of RNN/LSTM. Hands-on implementation for Caption/Summary generation from images/videos.

Understanding and implementation of Variational AutoEncoder on MNIST dataset. We will see how to encode images in a latent space of lower dimensions.

Is it possible to generate new images which never existed? Understanding and implementation of Generative Adversarial Networks on benchmark datasets.

Graph Neural Network

Are you ready to take your machine learning to the next level? Whether you want to build a recommender system for social media platforms or do drug prediction in biomedical, GNN has your back. We will see the Extension of Deep Learning on Graphs (GNN).
Introduction to several GNN variants GCN, GraphSage, etc