UNIT 1 Foundations of Generative AI

Introduction to Generative AI: Definition, working and applications of generative AI, Historical overview and recent advancements, Ethical considerations and societal impact. Probability and Statistics for Generative AI: Probability distributions and random variables, Maximum likelihood estimation, Bayesian inference and generative models

UNIT 2 Generative Models

Overview of generative models: Gaussian Mixture Models, Hidden Markov Models; Representation learning and latent variables; Auto encoders: Basics of auto encoders and their applications, Encoder and decoder architectures, Reconstruction loss and latent space representation

UNIT 3 Generative Adversarial Networks and Flow-based Models

Generative Adversarial Networks (GANs): Introduction, Architecture of GANs, Training GANs and understanding the loss functions; Autoregressive Models Flow-based generative models and their advantages, Normalizing flows and invertible transformations, Training and sampling from flow-based models

UNIT 4 Applications and Future Directions

Real-World Applications of Generative AI: Image synthesis and editing, Data augmentation and data generation, Generative AI in healthcare, gaming, and art; Emerging Trends and Future Directions: Reinforcement learning and generative models, Meta-learning and few-shot generation, OpenAI’s DALL-E.

UNIT 5 Ethical Issues And Limitations of Generative AI

Limitations of Generative AI,Issues and concerns,Considerations for Responsible Generative AI,Economic Implications,Social Implications,Future and professional Growth of Generative AI

Reference Book:

1. Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville , The MIT Press
2. Pattern Recognition and Machine Learning by Christopher M. Bishop
3. Natural Language Processing with Python” by Steven Bird, Ewan Klein, and Edward Loper
4. Steve Tingiris, Exploring GPT-3, Packt Publishing Ltd. UK, 2021
5. Sabit Ekin, Prompt Engineering For ChatGPT: A Quick Guide To Techniques, Tips, And Best Practices, DOI:10.36227/techrxiv.22683919.v2, 2023
6. Joseph Bab**** Raghav Bali, Generative Al with Python and TensorFlow 2, Packt Publishing Ltd. UK, 2021

 Text Book:

1. Introduction to Generative AI by Maggie Engler, Numa Dhamani February 2024 Publisher(s): Manning Publications ISBN: 9781633437197
2. Generative Deep Learning, by David Foster, 2nd Edition, O’Reilly Media, Inc.