INFO 5375 - Machine Learning for Health

Spring. 3 credits. Letter grades only.

Prerequisite: basic knowledge of machine learning, algorithms and python programming. Enrollment limited to: Cornell Tech Students. Offered in New York City at Cornell Tech.

Staff.

This course introduces students the various real-world health related problems such as patient screening, risk modeling, disease subtyping and precision medicine, along with their associated data, such as patient clinical records, medical images, physiological and vital signals from wearable sensors, multi-omics, etc. and how to use appropriate machine learning algorithms to analyze these data and help with the corresponding real-world health problems. The machine learning techniques involved in this class include classic supervised and unsupervised learning, network analysis, probabilistic modeling, deep learning, transfer learning, federated 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.

Outcome 1: Students will be able to understand the real health problems, identify potential beneficiaries and stakeholders, and formulate these problems under machine learning frameworks.

Outcome 2: Students will be able to understand the different types of health data captured in different scenarios, and identify appropriate machine learning pipelines to analyze them and obtain desired results.

Outcome 3: Students will be able to demonstrate the process and results of the machine learning to health professionals in appropriate ways, and capture their feedbacks to further improve the model.