Course Description

Healthcare is one of the most exciting application domains of artificial intelligence, with transformative potential in areas ranging from medical image analysis to electronic health records-based prediction and precision medicine. This course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. We will start from foundations of neural networks, and then study cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. In the latter part of the course, we will cover advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness. The course aims to provide students from diverse backgrounds with both conceptual understanding and practical grounding of cutting-edge research on AI in healthcare.

Staff Email

biods220-aut2223-staff@lists.stanford.edu

Course Time and Location

Mondays and Wednesdays, 1:30-3:00 PM
Alway Building, Room M106 (M112 on 10/17, 10/19, 10/21, 11/16)

Section Time and Location

Fridays, 1:30-3:00pm
Alway Building, Room M106

Sections will be used for various review sessions throughout the quarter. They will happen on a few Fridays, not weekly. Check the calendar under syllabus to stay up to date as to when review sessions will be held.

Instruction Format

Lectures, sections, and office hours for this course will be held in person, although this may change based on the evolving Covid-19 situation. Please check Stanford Health Alerts for updated guidelines.

Prerequisites

Proficiency in Python, or significant experience with a different programming language and ability to self-learn. Python will be used for homework assignments and the course project. Basic familiarity with college calculus (e.g. Math 19 or 41, comfortable taking derivatives), linear algebra (e.g. Math 51 or EE 103 / CME 103, comfortable with common matrix vector operations and notation), and probability and statistics (e.g. CME 106 or CS 109, comfortable with common probability distributions, mean, standard deviation, etc). Familiarity with machine learning, e.g. comfortable with the framework of machine learning and experience training a machine learning model. Familiarity with deep learning is highly recommended, e.g. prior experience training a deep learning model.