Python機器學習在智慧醫療的應用

1. Introduction: The role of AI in Digital Medicine and Public Health 2. Getting started with Python programming tools 3. Python programming: basics 4. Python programming: class and debugging 5. Introduction to Machine Learning 6. Python Machine Learning: numpy and scikit-learn 7. Python Machine Learning pipeline 8. Introduction to Deep Learning 9. Basic Python Deep Learning with Keras 10. Utilizing Deep Learning pretrained models 11. Medical image preprocessing 12. Deep Learning with medical images 13. Clinical Text preprocessing 14. Deep Learning with clinical texts 15. Deep Learning advanced topics 16. Student assignment presentation

透過人工智能的技術,來輔助醫療人員在臨床上做判讀、診斷、開藥、甚至是預警,已成為近年來醫院發展重點之一。人工智能的核心技術是「機器學習」。本課程的目標是給有興趣對機器學習一探究竟的醫學院學生們一個入門的管道:從Python程式語言教起,透過醫療數據的實例,以實作的方式帶領學生進入「機器學習」的世界,最後讓學生們進入「機器學習」中目前最熱門的領域「 深度學習」。 本課程的內容皆以Python程序語言設計,請學生自備筆記型電腦。期末作業可選擇獨自完成或分組。 Artificial Intelligence has shown great success in a wide range of smart healthcare and public health applications, from diagnosis and prescription suggestions, patient monitoring, disease prediction, to pandemic spread prediction. The goal of this introductory course is to provide an entry point for students who are interested in applying AI to solving healthcare related problems. We will start from Python programming basics, proceed to Machine Learning using real-world healthcare data, and then enter the world of Deep Learning, the hottest subfield of Machine Learning. The course is designed to be mainly a hands-on programming course accompanied with lectures explaining basic concepts, so students are required to bring their own laptops to class. The most basic laptop with WIFI capability will be sufficient, since we will be using an online programming platform. Students can choose to do the final assignment alone or in a group.

課堂練習50% 期末作業與報告50%In-class practice 50% Final assignment and presentation 50%