Mathematical Foundations of Generative AI

Overview

Syllabus 9

1. Week 1 Introduction
   • Introduction
   • Tutorial 1 : Introduction to Python Basics
   • Tutorial 2 : Introduction to Numpy
2. Week 2 Generative Models : Problem Formulation
   • Generative Models : Problem Formulation
   • Tutorial 3 : PyTorch Basics
   • Tutorial 4 : CNNs using PyTorch
   • Tutorial 5 : RNNs using PyTorch
   • Tutorial 6 : Transfer Learning with PyTorch
3. Week 3 Review of Probability and Machine Learning
   • Tutorial 7 : Review of Basic Probability 1
   • Tutorial 8 : Review of Basic Probability 2
   • Tutorial 9 : Review of Basic Probability 3
   • Tutorial 10 : Review of Machine Learning 1
4. Week 4 f- Divergence
   • f-Divergence and Examples
   • Tutorial 11 – f-Divergence and Examples
5. Week 5 GANs
   • Variational Divergence Minimization (VDM)
   • Generative Adversarial Networks (GANs)
   • Tutorial 12 : Implementations of Vanilla GAN, DCGAN and Conditional GAN
6. Week 6 GANs and VAEs
   • Wasserstein GAN (WGAN)
   • Inversion with GANs and FID
   • Latent Variable Models and Introduction to Variational Autoencoder (VAE)
7. Week 7 VAEs
   • VAEs Part 1
   • VAEs Part 2
   • Beta- VAE
   • Vector Quantised VAE
   • Introdution to Diffusion models
8. Week 8 Diffussion Models
   • Diffussion Models - Part 1
   • Diffussion Models - Part 2
   • Tutorial 13 : Wasserstein GAN (WGAN) Implementation using Gradient Clip
   • Tutorial 14 : Wasserstein GAN (WGAN) Implementation using Gradient Penalty
   • Tutorial 15 : VAE and Beta-VAE Implementation
9. Week 9 Diffusion Models contd.
   • Pedagogy in the Times of AI
   • Diffussion Models - Part 3
   • Diffussion Models - Part 4
   • Tutorial 16 : Implementation of VQ-VAE
   • Tutorial 17 : Implementation overview of Diffusion Models

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Before you start

Probability, Course in Machine Learning

Taught by

• Prof. Prathosh A. PIISc Bangalore