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Cloud and Machine Learning
9:00 - 12:00, August 12 - 16, 2019
Room 202, Astronomy-Mathematics Building, NTU

I-Hsin Chung (IBM T. J. Watson Research Center)

Weichung Wang (National Taiwan University)

一、 課程背景與目的:
Medical AI Research Collaboration Hub (MARCH) is a year-long educational and research development program aimed to guide students and researchers along medical artificial intelligence projects with real-world impact for healthcare patients and providers around the world. The program is organized around a workshop series in key topic areas to provide participants with comprehensive technical knowledge and thought leadership in the area of medical AI. Throughout the program, participant teams will form and be mentored by the organizers and invited faculty as they develop their project. Participants will have access to a broad range of resources provided by our sponsor and organizing institutions, including events held both in Taiwan and the United States.
Our goal is to provide an environment where true international, interdisciplinary collaboration can foster the development of new AI technologies to meet critical needs in healthcare. We believe strongly that success in this field requires long-term, deep collaboration between medicine, mathematics, and computer science. Our program is therefore open to physicians, researchers, students, and scientists, all of whom have a key role to play in the future of healthcare technology.
二、 課程講者:
I-Hsin Chung received the Ph.D. degree in computer science from the University of Maryland, College Park, in 2004, prior to joining IBM Research. After completing his Ph.D., Dr. Chung joined the IBM Research as a research scientist and worked on performance modeling, tuning and tools. Dr. Chung’s research is in the system architecture area including data-centric computing and high-performance computing. He is currently leading the efforts to co-design for future data center system with the strategic application workloads such as cognitive and cloud computing. His experience includes performance analysis and modeling on IBM platforms such as POWER, mainframe Z Systems, and the Blue Gene systems. He has worked in the system software and performance analysis of world-renowned CORAL and Blue Gene series supercomputer designs. He is also an adjunct faculty of Courant Institute at NYU. He has over 70 published and creative
works in top journals and conferences including IEEE TPDS, ACM TOM, JPDC, SC, and IPDPS.
三、 課程之大綱:
The short course is an advanced graduate course in cloud computing and machine learning. This course exposes students to various cloud computing models and introduces them to performing machine learning on the cloud. The course material introduces students to cloud providers and their machine learning service capabilities. Students will learn how to build cloud systems for machine learning, the application characteristics, and develop hands-on experience with programming machine learning applications on these cloud platforms.
四、 課程詳細時間:
Tentative schedule (9:00AM-12PM/day)
8/12 Introduction to cloud computing & neural network
8/13 Virtual machine & hands-on
8/14 Container & hands-on
8/15 Performance analysis & hands-on
8/16 Invited talk: AWS

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