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
113-1 
授課對象
電機資訊學院  電機工程學研究所  
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
星期三7,8,9(14:20~17:20) 
上課地點
共103 
備註
本課程以英語授課。
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) 
預期每週課前或/與課後學習時數
 
Office Hours
 
指定閱讀
 
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
09/04  Course announcement
Video record: AIIM-wk1-intro AI and IM 
Week 2
09/11  Pre-class:
1. The rise of artificial intelligence in healthcare applications
2. Machine learning basics
3. DFN basics
Case study: DNN for NSCLC prognosis prediction (genetic data)

In-class:
Group discussion (pre-class materials)
 
Week 3
09/18  Pre-class:
1. CNN
2. RNN

In-class:
Group discussion (pre-class materials) 
Week 4
09/25  In-class:
Workshop: Gene expression and system biology feature selection
Workshop: An illustration of medical images/time series processing 
Week 5
10/02  In-class:
Professor Guo, Yue-Liang introduces their open topics for the FP
Doctor Ting, Sze-Ya/Doctor Chang, Pei-Yeh introduces their open topics for the FP 
Week 6
10/09  Pre-class:
Transformer-1
Transformer-2

In-class:
Group discussion (pre-class materials)
 
Week 7
10/16  Domain expert talk and panel discussion 
Week 8
10/23  In-class:
Midterm presentation: Final project proposal 
Week 9
10/30  In-class:
Workshop: Evaluation metrics, decision threshold, and visualization 
Week 10
11/06  Pre-class:
Graph Neural Networks (GNN)

In-class:
Group discussion (pre-class materials)
Group discussion (for the final project) 
Week 11
11/13  Pre-class:
Case study (X-RIM)
Case study (EMBC'24 gene paper)
Case study (EMBC'24 BCLC paper)

In-class:
Case study (X-RIM)
Case study (EMBC'24 gene paper)
Case study (EMBC'24 BCLC paper)
Group discussion (for the final project) 
Week 12
11/20  Domain expert talk 2 and panel discussion
 
Week 13
11/27  In-class:
Midterm presentation: Paper survey
 
Week 14
12/04  No class 
Week 15
12/11  Domain expert talk 3 and panel discussion  
Week 16
12/18  In-class:
Mini-workshop of final presentation