• Fast-paced introduction to deep learning, the engine behind modern artificial intelligence, with an emphasis on developing a practical understanding of how to build models to solve complex problems involving unstructured data. Rapid introduction to deep neural networks and their training. Emphasis on language models, focused on masked and generative modeling and vision models (using diffusions and transformers). Model adaptation via fine-tuning, RL-based training (eg., RLHF, GRPO, etc.). System design concepts including retrieval augmented generation and agentic system design. Prior familiarity with Python and fundamental machine learning concepts (such as training/validation/testing, overfitting/underfitting, and regularization) required. Preference to Sloan Master of Business Analytics students.

• Textbook Information

There is no information available for this course.