BIODS220 (CS271, BIOMEDIN220)
Artificial Intelligence in Healthcare
Course DescriptionHealthcare 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.
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Course Time and LocationMondays and Wednesdays, 1:30-2:50pm
PrerequisitesProficiency 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). Prior experience with machine learning is highly recommended (e.g. comfortable with the framework of machine learning, and prior experience training a machine learning model). Prior experience with deep learning is helpful but not required.
Assignment 1: 20%
Assignment 2: 20%
Course project: 40%
AssignmentsThere will be two homework assignments in the class, each worth 20% of the final grade. A large part of these assignments will include programming and practical exercises to build expertise in using deep learning with medical data.
MidtermThe in-class midterm is worth 20% of the final grade and will test conceptual understanding of medical data types and designing deep learning approaches for working with medical data.
Course ProjectThe course project is a significant component of the course and will be worth 40% of the final grade. Students will work individually or in teams of up to 3 to develop and implement an AI-based approach to a healthcare problem. The project will span the duration of the quarter and will include a project proposal, milestone report, and final presentation and report.
|Lecture 1||Jan 6 (Mon)||Course Introduction|
|Lecture 2||Jan 8 (Wed)||Deep Learning Fundamentals: Part 1|
|Lecture 3||Jan 13 (Mon)||Deep Learning Fundamentals: Part 2|
|Lecture 4||Jan 15 (Wed)||Medical Images: Classification|
|Jan 20 (Mon)||MLK Holiday|
|Lecture 5||Jan 22 (Wed)||Medical Images: Detection and Segmentation|
|Lecture 6||Jan 27 (Mon)||Medical Images: Higher-Dimensional and Video Data|
|Lecture 7||Jan 29 (Wed)||Electronic Health Records|
|Lecture 8||Feb 3 (Mon)||Electronic Health Records: Advanced Topics|
|Lecture 9||Feb 5 (Wed)||Genomics|
|Lecture 10||Feb 10 (Mon)||Genomics: Advanced Topics|
|Lecture 11||Feb 12 (Wed)||Time-series and Multimodal Data|
|Feb 17 (Mon)||Presidents' Day Holiday|
|Lecture 12||Feb 19 (Wed)||Unsupervised and Reinforcement Learning|
|Feb 24 (Mon)||In-Class Midterm|
|Lecture 13||Feb 26 (Wed)||Fairness and Transparency|
|Lecture 14||Mar 2 (Mon)||Distributed Computing, Privacy, and Security|
|Lecture 15||Mar 4 (Wed)||Guest Lecture: Special Topics|
|Lecture 16||Mar 9 (Mon)||Guest Lecture: Special Topics|
|Lecture 17||Mar 11 (Wed)||Course Conclusion|