Healthcare Lab: Introduction to Healthcare Delivery in the United States
Focuses on the business challenges and opportunities to deliver high-quality and reasonably-priced health services, mainly in the United States. Provides an opportunity to interact with guest speakers and senior executives from the health sector. Topics include aspects of healthcare delivery operations and how they are affected by healthcare reform policies, alternative payment models, population health perspectives, and social determinants of health. Discussions include examples from the ongoing healthcare-related work of Sloan faculty, as well as the potential for analytics and digitization to impact healthcare delivery. Provides a broad perspective on various career paths, such as consulting, entrepreneurship, delivery system management, and digital innovation development. Student teams work with a provider, supplier or healthcare-related startup organization on an applied project. Includes on-site work during fall and IAP.
Spring 2024 · Hands-On Deep Learning
Topics
- Introduction to neural networks and deep learning (motivation, smart representations, layers, activations, architectures); training deep NNs (loss functions, gradient descent, regularization)
- Training deep NNs (continued); introduction to Keras/Tensorflow (tensors, functional API, training, evaluation); application to tabular data
- Deep learning for computer vision—building convolutional neural networks from scratch
- Deep learning for computer vision—transfer learning and fine-tuning; introduction to HuggingFace
- Deep learning for natural language—the basics (tokenization, n-grams, bag-of-words)
- Deep learning for natural language—embeddings
- Deep learning for natural language—transformers
- Deep learning for natural language—transformers, self-supervised learning
- Generative AI—large language models (LLMs) and retrieval augmented generation (RAG)
- Generative AI—adapting LLMs with parameter-efficient fine-tuning
- Generative AI—text-to-image models
- Project presentations
Readings
Prerequisites & course information
Prereq: ( 15.060 and 15.761 ) or permission of instructor G (Fall, IAP) 4-0-11 units Credit cannot also be received for 15.767
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Spring 2024
- MIT OCW syllabus ↗
- MIT OCW schedule ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.