課程資訊
課程名稱
人工智慧與智慧醫療
Artificial Intelligence and Intelligent Medicine 
開課學期
111-2 
授課對象
學程  智慧醫療學分學程  
授課教師
林 澤 
課號
CommE5064 
課程識別碼
942EU0780 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期五7,8,9(14:20~17:20) 
上課地點
共103 
備註
本課程以英語授課。上課地點:共同103未來教室。智慧醫療學分學程所屬電資學院數據領域課程。
總人數上限:60人 
 
課程簡介影片
 
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課程大綱
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課程概述

Artificial intelligence (AI) can be applied to a wide range of areas. Notably, the fast-growing intelligent medicine area has created tremendous business opportunities recently. It also creates an ideal environment for AI-Biomedical interdisciplinary specialists to make considerable contributions and significantly impact the world. Intelligent medicine aims to utilize state-of-the-art AI technologies for many medical applications such as accurate disease risk prediction and essential predictors selection, which are for early precise and efficient treatments. In this course, we will introduce the vast potential of intelligent medicine and help students advance their skills in this area, and motivate them to become AI-Biomedical interdisciplinary scientists.

In addition, in this course, we will introduce potential partners for future interdisciplinary collaboration to our students and provide opportunities for practical implementations through several carefully designed experiments, which shall demonstrate how to leverage real-world medical resources and related AI technologies. Meanwhile, we plan to arrange visits to prestigious companies and institutes and several seminars given by domain experts to further inspire and motivate our students. 

課程目標
1. To polish skills for integration of programming, medical data analysis, and machine learning.
2. To train intelligent medicine specialists through practical implementations and connect them to potential future collaborators.
3. Promote the collaboration between the College of EECS and Medicine. 
課程要求
Required pre-request:Machine Learning
Recommended pre-request: Python Programming for Intelligent Medicine (智慧醫療程式設計) or Special Topics in Innovative Integration of Medicine and EECS (醫學電資整合創意專題) 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
Lectures will be based on lecture notes and slides. We will try to flip classroom this year! 
參考書目
1. “Machine Learning and AI for Healthcare: Big Data for ImprovedHealth Outcomes,” 2nd Edition, by Arjun Panesar.
2. “Artificial Intelligence in Healthcare: AI, Machine Learning, and Deepand Intelligent Medicine Simplified for Everyone,” by Dr. Parag SureshMahajan MD.
3. “Artificial Intelligence in Healthcare,” edited by Adam Bohr andKaveh Memarzadeh. 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
Week 1
2/24  Introduction to AI and intelligent medicine / Introduction to Machine Learning and Deep Learning 
Week 2
3/3  The rise of artificial intelligence in healthcare applications 
Week 3
3/10  Machine learning basics I II / Team up for the final project 
Week 4
3/17  Workshop: IDE construction and essential packages instruction 
Week 5
3/24  Machine learning basics III/ Deep Feedforward Network Basics  
Week 6
3/31  Workshop: data preprocessing 
Week 7
4/7  lecture / Domain expert talk and panel discussion 
Week 8
4/14  Midterm: Proposal Presentation 
Week 9
4/21  Recurrent Neural Networks 
Week 10
4/28  Workshop: implementation of time-series model for EHRs
Visualization and Interpretability 
Week 11
5/5  Transformer and learning representations/BERT and transfer learning 
Week 12
5/12  lecture / Domain expert talk and panel discussion 
Week 13
5/19  Workshop: visualization and interpretability 
Week 14
5/26  lecture / Domain expert talk and panel discussion  
Week 15
6/2  Recent topics on AI in intelligent medicine  
Week 16
6/9  Mini workshop of final presentation