Provide an in-depth understanding of Large Language Models (LLM) and Generative AI. Develop skills necessary to implement and use LLM in various application contexts. Explore the ethical implications and social impact of using LLM and Generative AI.

Understand the fundamental principles of LLM and Generative AI. Ability to implement language models using machine learning libraries. Analyze and evaluate the performance of LLM in various tasks. Identify and discuss the ethical considerations related to the use of LLM.

Introduction to LLM and Generative AI (4 hours) Language Model Architectures (8 hours) Recurrent Neural Networks (RNN) Transformers and BERT Training LLM (8 hours) Datasets and preprocessing Optimization techniques Applications of LLM (8 hours) Text generation Machine translation Chatbots and virtual assistants Tools and Frameworks (4 hours) TensorFlow and PyTorch Specific NLP libraries Evaluation and Fine-tuning (4 hours) Evaluation metrics Fine-tuning techniques Ethics and Social Impact (4 hours) Bias in language models Social impact and regulation Practical Project (8 hours) Implementation of an LLM Project presentation

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, Aaron Courville "Natural Language Processing with PyTorch" by Delip Rao, Brian McMahan Articles and online resources on LLM and Generative AI

Lectures Practical lab exercises Group discussions and case study analysis Practical projects and presentations

Oral presentations Active participation in classes and discussions

Ability to apply the knowledge acquired during the course Clarity of exposition and argumentation skills