IIT Delhi Artificial Intelligence and Machine Learning for Industry - Batch 08

• Foundations of Python Programming

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

Learning Outcome:

• Learners will develop a comprehensive understanding and application of linear algebra concepts, probability and statistics, and optimisation in real-world machine learning tasks.

• 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

Project

• Regression application

Learning Outcome:

• Learners will master simple and multiple linear regression, non-linear regression, and the least squares approach, gaining practical experience through hands-on demos. They will also learn model selection, regularisation, and the bias-variance trade-off, culminating in a regression application project discussion.

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

Learning Outcome:

• Learners will develop expertise in logistic regression, decision trees, random forests, and support vector machines, gaining practical experience through hands-on demos. They will also learn clustering methods and the interpretability of machine learning models, culminating in a classification application project discussion.

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

• Concept of Transformers and its Application to NLP

Generative AI

• Introduction to Generative AI and LLM Models

Project

• Deep learning application

Learning Outcome:

• Master the fundamentals of neural networks, including feedforward networks, training, and backpropagation, with practical experience through hands-on demos. Additionally, learn advanced topics such as convolutional neural networks, recurrent neural networks, graph neural networks, transformers, and generative AI, applying these concepts to real-world applications.

1. 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

2. Logistic Regression Lab

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

3. Decision Tree, Random Forest, XGBoost

• In-depth analysis of algorithms on benchmark datasets

4. Support Vector Machines

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

5. 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

6. 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

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

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

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

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

10. 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

11. Natural Language Processing

• Text Summarisation

12. Course Project

• Build your own recommender system using any of the discussed techniques (GNN, CNN, LSTM, classical ML, etc.)