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Genai Foundations

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Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction to Probabilistic Deep Generative Modelling

Incomplete

02W02

Generative Modelling via variational Divergence Minimization

Incomplete

03W03

Generative Adversarial Networks: Part 1 (Introduction and Formulation)

Incomplete

04W04

Generative Adversarial Networks: Part 2 (WGANs and Applications)

Incomplete

05W05

Generative Modelling via Variational Auto Encoding

Incomplete

06W06

Variational Auto Encoders: Improvisations and VQVAE

Incomplete

07W07

Denoising Diffusion Probabilistic Models (DDPMs) - Formulation

Incomplete

08W08

Diffusion Models: Multiple forms and Implementation

Incomplete

09W09

Conditional Diffusion Models and Score-based models

Incomplete

010W10

Auto-Regressive Models and Large Language Models Introduction

Incomplete

011W11

LLMs: Models, Sampling, Inference and Quantization Methods

Incomplete

012W12

LLMs – Reinforcement Learning based Alignment Methods (PPO, DPO)

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

BSDA5002 — Generative AI Foundations

Open this first for the core logic and traps.

Dive in

4 min

Generative Adversarial Networks: Theory and Implementation

Open this first for the core logic and traps.

Dive in

5 min

Conditional Generation and Classifier-Free Guidance

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

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Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSDA5002

PG / MTech

4 Credits

Mathematical Foundations of Generative AI

This course provides an in-depth exploration of deep generative models, including their probabilistic foundations and learning algorithms. Student...

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Execution Protocol

Module 0

Topic

Module 1

Introduction to Probabilistic Deep Generative Modelling

Module 2

Generative Modelling via variational Divergence Minimization

Module 3

Generative Adversarial Networks: Part 1 (Introduction and Formulation)

Module 4

Generative Adversarial Networks: Part 2 (WGANs and Applications)

Module 5

Generative Modelling via Variational Auto Encoding

Module 6

Variational Auto Encoders: Improvisations and VQVAE

Module 7

Denoising Diffusion Probabilistic Models (DDPMs) - Formulation

Module 8

Diffusion Models: Multiple forms and Implementation

Module 9

Conditional Diffusion Models and Score-based models

Module 10

Auto-Regressive Models and Large Language Models Introduction

Module 11

LLMs: Models, Sampling, Inference and Quantization Methods

Module 12

Syllabus

Topic

Introduction to Probabilistic Deep Generative Modelling

Generative Modelling via variational Divergence Minimization

Generative Adversarial Networks: Part 1 (Introduction and Formulation)

Generative Adversarial Networks: Part 2 (WGANs and Applications)

Generative Modelling via Variational Auto Encoding

Variational Auto Encoders: Improvisations and VQVAE

Denoising Diffusion Probabilistic Models (DDPMs) - Formulation

Diffusion Models: Multiple forms and Implementation

Conditional Diffusion Models and Score-based models

Auto-Regressive Models and Large Language Models Introduction

LLMs: Models, Sampling, Inference and Quantization Methods

LLMs – Reinforcement Learning based Alignment Methods (PPO, DPO)

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Mathematical Foundations of Generative AI

Execution Protocol

Topic

Introduction to Probabilistic Deep Generative Modelling

Generative Modelling via variational Divergence Minimization

Generative Adversarial Networks: Part 1 (Introduction and Formulation)

Generative Adversarial Networks: Part 2 (WGANs and Applications)

Generative Modelling via Variational Auto Encoding

Variational Auto Encoders: Improvisations and VQVAE

Denoising Diffusion Probabilistic Models (DDPMs) - Formulation

Diffusion Models: Multiple forms and Implementation

Conditional Diffusion Models and Score-based models

Auto-Regressive Models and Large Language Models Introduction

LLMs: Models, Sampling, Inference and Quantization Methods

LLMs – Reinforcement Learning based Alignment Methods (PPO, DPO)

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