Generative AI stands at the cutting edge of today's artificial intelligence landscape, ushering in a new paradigm where machines understand intricate data patterns and autonomously produce them. This in-depth course ventures into the fascinating world of Generative AI, cultivating a deep understanding of its potential to adeptly create, communicate, and innovate across diverse data forms. Students will gain hands-on experience with some of today's most renowned models, adapting them to unique use cases while engaging with a vast array of topics—from foundational theories and principles to design, hands-on implementation, and thorough analysis of these systems. By the course's end, participants will be equipped to transition into the industry with tangible skills and a robust portfolio, contribute meaningfully to academic discourse by augmenting existing research or pioneering novel concepts, or embark on personal projects with an enhanced perspective and expertise

The goal of this course is to get the students excited about Machine Learning and to enable them to:

• Be able to utilize foundation models and tune them in order to achieve a well-defined goal

• Understand the theory of the ideas behind, and the applications of modern generative systems including Large Language Models

• Get hands-on experience with the creation of AI-powered applications

• Gain a firm grip on expertly critiquing and analyzing the entire pipeline of a system

By the end of the course, students should be able to:

• Understand the foundations and core ideas of modern Machine Learning architectures and models

• Appreciate the scope of datasets and variety of training mechanisms

• Learn core ideas in creating applications using these models, including use-cases and practical details

• Understand the evolution of foundation models in terms of techniques, scale, working mechanisms etc.

Textbooks

• Speech and Language Processing by Jurafsky and Martin, 3rd edition

Our special thanks to Professors Roni Rosenfeld, Kilian Weinberger, Andrew Ng, Sarmad Hussain, Dan Jurafsky, James H. Martin, Christopher Manning, and Victor Levrenko, whose Machine Learning, Natural Language Processing, and Speech Processing courses inspired the contents of several lectures in this series. 

We would also like to express our gratitude towards Kalid Azad (Better Explained), Joshua Starmer (StatQuest), and Grant Sanderson (3blue1brown), as their amazing educational videos motivated and simplified several complex explanations in this course.