Course

Unit 1

Introduction to Generative AI, Autoencoders – Representational power, layer size and depth, Undercomplete autoencoders, Denoising autoencoders, Contractive autoencoders, Variational autoencoders, Case study: Applications of autoencoders in dimension reduction.

Unit 2

Generative Adversarial networks (GAN) – structure and training algorithm, Deep Convolutional GAN, Autoregressive models – Finite memory, long range memory through RNN and CNN, Transformers – Encoder, decoders, scaling laws, Case study: Generative Adversarial Networks-aided Intrusion Detection System

Unit 3

Structured probabilistic models – Issues of unstructured models, Directed and Undirected Graphs to describe the models, Partition function, separation and D-separation, Conversion of graphs, sampling from graphical models, Case study: Restricted Boltzmann machine.

• This course covers the mathematical and computational foundations of generative modeling, as well as applications.
• Specific topics include variational autoencoders, generative adversarial networks, autoregressive models such as Transformers, normalizing flow models, information lattice learning, neural text decoding, prompt programming, and detection of generated content.

• CO1: Understand principles of Generative AI and their applications.
• CO2: Analyze Autoencoder and transformer in real-world scenarios.
• CO3: Analyze GAN architectures and applications.
• CO4: Analyze graphs for probabilistic models.

CO-PO Mapping

• Goodfellow, Y. Bengio, and A. Courville, “Deep Learning”, MIT Press, 2016.

• Raut, R., Pathak, P. D., Sakhare, S. R., & Patil, S. (Eds.), “Generative Adversarial Networks and Deep Learning: Theory and Applications”. CRC Press, 2023.
• M. Tomcsak, “Deep Generative Modeling”, Springer, 2022
• Langr J, Bok V. “GANs in action: deep learning with generative adversarial networks”. Manning. 2019.
• Papoulis and S. U. Pillai, “Probability – Random Variables, and Stochastic Processes”, Fourth Edition, McGraw-Hill, 2017.

Evaluation Pattern: 70:30

*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation