Programme Content
Foundations of Python Programming
2. Mathematical Foundations for AI/MLMotivations and Introduction to Different ML Paradigms
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
Model Selection, Regularisation, and Bias-Variance Trade-off
M2 Project: Regression Application
4. Classical Machine LearningMotivation 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
Model Selection
Model Selection, Regularisation, and Bias-Variance Trade-off
M2 Project: Regression Application
5. Deep LearningConvolutional 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
IIT_Delhi_AIMLI09_Brochure.pdf
IIT_Delhi_AIMLI09_Brochure.pdf
Graph Neural Networks
Introduction to Graph Data
Graph Neural Network Architecture
Hands-on Demo
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
Introduction to Agentic AI Concepts: tools, memory, functions, and frameworks. (Will be covered in one lecture)
01. 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.
02. Logistic Regression Lab
Sentiment analysis of consumers. Can we directly infer the quality of any product based on its reviews?
03. Decision Tree, Random Forest, XGBoost
In-depth analysis of algorithms on benchmark datasets.
04. Support Vector Machines (SVM)
Image classification on fashion MNIST dataset, intuition of soft margin, hard margin, solving SVM using CVXPY.
05. 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.
06. Convolutional Neural Network (CNN)
Ever wondered how computers identify faces? We will see how CNN has revolutionised the field of computer vision.
Understanding layers, visualisation of the learning process, occlusion, and GRADCAM.
07. Sequential Model (Recurrent Neural Network/Long Short-Term Memory)
Implementation of RNN/LSTM. Hands-on implementation for caption/summary generation from images/videos.
08. Variational AutoEncoder
Understanding and implementation of Variational AutoEncoder on MNIST dataset. We will see how to encode images in a latent space of lower dimensions.
09. Generative Adversarial Networks
Is it possible to generate new images which never existed? Understanding and implementation of Generative Adversarial Networks on benchmark datasets.
10. Graph Neural Networks (GNN)
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 like, GCN, GraphSage, etc.
11. Natural Language Processing
Text summarisation.
12. Generative AI
Fine-Tuning SLMs and LLMs and Their Integration with Downstream Tasks.
13. Course Project
Build your own recommender system using any of the discussed techniques (GNN, CNN, LSTM, classical ML, etc.).
IIT_Delhi_AIMLI09_Brochure.pdf
