Curriculum
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Descriptions
MLDS 424: Generative AI


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Description

This course offers an in-depth exploration of Generative AI, focusing on genAI fundamentals, practical implementations, and cutting-edge advancements in the field. Students will delve into key topics such as Vector Databases (Vector DB), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), prompt engineering, fine-tuning techniques like Low-Rank Adaptation (LoRA), intelligent agents, foundational models, Generative Adversarial Networks (GANs), and Variational Autoencoders (VAEs). The course combines lectures on software frameworks, models, and algorithms with hands-on projects to provide students with a comprehensive understanding of Generative AI and its diverse applications. 

Learning Objectives
By the end of this course, students will be able to:

  1. Understand the fundamental concepts and techniques in Generative AI.
  2. Understand kNN-based, clustering and hashing concepts driving vector databases.
  3. Apply Vector Databases and Retrieval-Augmented Generation to enhance AI models.
  4. Develop and fine-tune Large Language Models (LLMs) using prompt engineering and LoRA (zero vs few shot learning; in-context learning).
  5. Design and implement intelligent AI agents.
  6. Understand, implement and evaluate GANs and VAEs.
  7. Identify and utilize appropriate Generative AI techniques for various real-world applications.