INFO 5375: Machine Learning for Health
Jan 22-May 7 – Tuesdays and Thursdays 10:10am - 11:25am Bloomberg Center, Room 61XInstructor: Fei Wang (few2001@med.cornell.edu)
Course Objective: This course introduces students the various types of health data, including patient clinical records, medical images, physiological and vital signals from wearable sensors, multi-omics, etc. and how to use machine learning algorithms to analyze these data and help with real world health problems such as patient screening, risk modeling, disease subtyping and precision medicine. The technical topics to be introduced in this class include classic supervised and unsupervised learning, network analysis, probabilistic modeling, deep learning, transfer learning, algorithmic fairness and interpretability. We will also invite clinicians or researchers working in the health industry to deliver guest lecturers in the class. The students will gain hands-on experience on analyzing real world health data during course assignments and projects.
Credits: 3 credits.
Teaching Assistant: Subham Sahoo (ssahoo@cs.cornell.edu).
Course Format:
Prerequisites: Machine Learning, Python Programming
| Date | Content | Presenter | Materials | Assignments |
|---|---|---|---|---|
| 01/23/2024 | Introduction of machine learning for health | Fei Wang | ||
| 01/25/2024 | Overview of machine learning | Fei Wang | Reading Group formation due | |
| 01/30/2024 | Machine learning in clinical risk prediction | Fei Wang | Project Group formation due | |
| 02/01/2024 | Generalizability of machine learning models in clinical risk prediction settings | Suraj Ranjendran | ||
| 02/06/2024 | Algorithmic Bias | Fei Wang | ||
| 02/08/2024 | Algorithmic bias reading presentation | Students | Paper 1 (Group 1), Paper 2 (Group 10) | |
| 02/13/2024 | Model interpretation and explanation | Fei Wang | ||
| 02/15/2024 | Model interpretation and explanation reading presentation | Students | Paper 1 (Group 2), Paper 2 (Group 9) | |
| 02/20/2024 | In-Class Debate: Explaining the model or no? | Fei Wang | ||
| 02/22/2024 | Sepsis risk prediction reading presentation | Students | Paper 1 (Group 3), Paper 2 (Group 8) | Project Proposal Due |
| 02/27/2024 | Break | |||
| 02/29/2024 | Federated Learning | Fei Wang | ||
| 03/05/2024 | Project proposal presentation I | Students | ||
| 03/07/2024 | Project proposal presentation II | Students | ||
| 03/12/2024 | Large language models I | Fei Wang | ||
| 03/14/2024 | Large language models II | Fei Wang | ||
| 03/19/2024 | Human-Centered Approaches to Explaining and Interacting with AI Systems | Xingbo Wang | ||
| 03/21/2024 | Canceled | |||
| 03/26/2024 | Regulations and policies reading | Students | Paper 1 (Group 4), Paper 2 (Group 7) | |
| 03/28/2024 | Model auditing reading | Students | Paper 1 (Group 5), Paper 2 (Group 6) | Project Intermediate Report Due |
| 04/02/2024 | Break | |||
| 04/04/2024 | Break | |||
| 04/09/2024 | Beyond Detection: New Opportunities for AI in Mental Healthcare | Dan Adler | ||
| 04/11/2024 | Multi-modal machine learning in health sciences across academia and industry | Benjamin Glicksberg | ||
| 04/16/2024 | Implementation of AI models in clinical workflow | Yiye Zhang | ||
| 04/18/2024 | Machine learning for critical care | Edward Schenck | ||
| 04/23/2024 | Using Big Data and Machine Learning in Health Policy Research | Yongkang Zhang | ||
| 04/25/2024 | Applications of AI in Voice and Swallowing Disorders | Anais Rameau | ||
| 04/30/2024 | TBD | TBD | ||
| 05/02/2024 | Final Project Presentation I | Students | Final Project Presentation I | |
| 05/07/2024 | Final Project Presentation I | Students | Final Project Presentation II |
© Fei Wang