Degree Level Course
Mathematical Foundations of Generative AI
This course provides an in-depth exploration of deep generative models, including their probabilistic foundations and learning algorithms. Students will learn about various types of deep generative models such as variational autoencoders, generative adversarial networks, autoregressive models, Diffusion Models and Large Language Models. The course will cover both theoretical foundations and practical implementations of these models using popular frameworks like PyTorch. Students will gain hands-on experience through lectures and assignments, allowing them to explore deep generative models across various AI tasks.
Taught by
Prathosh A P
Code
BSDA5002
Credits
4 Credits
Type
Unspecified
Prerequisites
None
Core Competencies
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What You'll Learn
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Develop a deep understanding of the importance of generative models in artificial intelligence and machine learning.
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Design and implement generative models using popular frameworks.
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Implement a range of generative models, including autoregressive models, VAEs, GANs and Diffusion Models
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Build problem-solving skills by tackling challenges and complexities in the practical implementation of generative models.
12-Week Roadmap
Course Structure & Syllabus
For details of standard term assessment timelines and exam structures, visit our Academics page.
WEEK 1
Introduction to Probabilistic Deep Generative Modelling
WEEK 2
Generative Modelling via variational Divergence Minimization
WEEK 3
Generative Adversarial Networks: Part 1 (Introduction and Formulation)
WEEK 4
Generative Adversarial Networks: Part 2 (WGANs and Applications)
Reading List
Prescribed Books & References
- Foster D. Generative deep learning. " O'Reilly Media, Inc.; 2023
- Recent papers/surveys that are relevant to the course.
Faculty & Experts
About the Instructors