AI Development, Applications & Beyond

Achieve all the requirements below:
1. Attend all online courses
2. Join the online discussion session with professors
(18:30-19:30, April 26/ May 5/ May 13, Taiwan Time)
3. Take online quiz
4. Submit a short essay (500 words, by May 18, 2022)

Upon completion of the program requirements, participants will receive an official NTU transcript with ONE NTU short-term program credit from this 18- hour NTU Plus Academy AI Development and Application Program. Online courses will be offered from April 20 to May15, 2022.

Associate Vice President for International Affairs, NTU
Professor, Department of Electrical Engineering, NTU

Ph.D., Dept of Electrical Engineering, National Taiwan University

Organic Light-emitting Device, Display Technologies
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Course Title: Introduction of AI
Dr. Hung Yi Lee 李宏毅

Special Award:
The 59th Ten Outstanding Young Person Award in Science and Technology Research & Development of Taiwan (R.O.C.)

Position:
Associate Professor, Department of Electrical Engineering, NTU

Education:
Ph.D., Department of Electrical Engineering, NTU

Research and Specialty:
Machine learning (especially deep learning), spoken language understanding, and speech recognition

Course Introduction: 
Gmail knows if an email is spam, Facebook knows if a photo contains a human face, Siri understands and answers user questions, and Alpha Go is better than professional players. Humans do not directly design the programs in these applications. Instead, the machine can automatically learn from data. How to design algorithms that allow machines to learn automatically is the focus of this course.
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Course Title: Artificial Intelligence for Engineering Application
Dr. Chuin-Shan Chen 陳俊杉

Position:
Professor, Computer-Aided Engineering, Department of Civil Engineering, NTU
Professor, Department of Materials Science and Engineering, NTU
Associate Director, Center of Innovation and Synergy for Intelligent Home and Living Technology (iNSIGHT), NTU
Director, NTUCE-NCREE Joint Artificial Intelligence Research Center

Education:
Ph.D., School of Civil and Environmental Engineering, Cornell University

Research and Specialty:
Multiscale Modeling, Computational Mechanics, Materials Modeling, Artificial Intelligence for Engineering Application

Course Introduction:
AI for engineering applications is producing new and relevant solutions that continues transforming our society at a fast pace. The trend has a long runway and we are still in its early innings. In this talk, I will give a few examples, some conducted in our group, to highlight this emerging trend and discuss a few observations to move the field forward.
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Associate Professor, Department of Biomechatronics Engineering, NTU

Ph.D., Department of Chemical and Biomolecular Engineering, Rice University

Biomedical imaging science, Development of medical AI technology, X-ray phase contrast imaging, Tomographic image reconstruction, Agricultural applications of artificial intelligence, Development of rapid screening test kits.

Artificial intelligence (AI) is the emulation of human behavior in analyzing, interpretating, and comprehending complicated problems and data using a machine and algorithms. The modern applications of AI technology are ubiquitous. This course will cover the applications and innovations of in modern medicine and agriculture. We will introduce the workflow of medical data analytics and give a quick overview of the history of health-related application of AI. The current research and applications of AI in various aspects of medicine and healthcare will be covered. On the other hand, the popular and emerging trends of AI applications in agriculture and its impact in agricultural industries will be explored in this class. The AI engines, workflows and adaptation and validation for different applications will also be introduced.

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Associate Professor, Department of Computer Science and Information Engineering, NTU

Ph.D., Computer Science, UC Berkeley; Certificate in Management of Technology, Haas School of Business, UC Berkeley

Human-Computer Interaction, Mobile Computing, Cloud Computing, Ubiquitous Computing

This course highlights the evolution of human-computer interaction (HCI) over the last 50 years, and the integration of artificial intelligence (AI) to enable more intelligent user interfaces. Topics include novel sensing and actuation, and span technologies such as biometric authentication on smartphones that enables usable security, intelligent voice-based assistants that supports conversational interaction, computational photography that captures the world more like the human eyes, and virtual/augmented reality that reshape our senses. Examples are drawn from existing products as well as research prototypes that explore future human-computer interaction possibilities.

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Professor, Department of Electrical Engineering, NTU

Ph.D. Department of Electrical and Computer Engineering from Georgia Institute of Technology, Atlanta, Georgia, USA.

Internet of Things, next-generation communication systems, network science, and cybersecurity

Network science has emerged as the study of complex systems, in particular complex relational data and its applications. It draws on methods and theories from multiple disciplines including mathematics, computer science, physics, and sociology. This course provides an overview of network science and its applications in social networks and telecommunication networks. In particular, we introduce in this course the method of community detection and its application in the detection of Sybil attacks for computer security.

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Chair, Graduate Institute of Linguistics, NTU
Associate Professor, Graduate Institute of Linguistics, NTU
Researcher, Center for Artificial Intelligence and Advanced Robotics

Ph.D. Computational Linguistics, University of Tubingen, Germany

Corpus and Computational Linguistics / Lexical Semantics / Morphology / Linguistic Philosophy and Pragmatics / Cognition and Language

This course provides a brief introduction to Data Science and Text Analytics with AI/NLP (Natural Language Processing) technologies. We are now living in a world increasingly overwhelmed by data. Data science, broadly taken as the science of extracting meaningful information from data is gaining prominence in understanding the world and society we live in. And as unstructured data is the most abundant type of data ever generated on a daily basis, the analytic process that is used for deriving high-quality information from text, often called text analytics or text mining, plays an important role in data science.
The latest AI/NLP techniques have fundamentally reshaped our understanding of data, and are already empowering the new generation of data scientists. In this course, we will introduce the basic idea of Data Science, and explain how AI, Machine Learning, and NLP are making it far more feasible to automate many data analytics processes, including related tools and resources. Some real applications will be demonstrated.
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Dr. Chiou-Shann Fuh 傅楸善
 
Position:
Professor, Department of Computer Science and Information Engineering, NTU

Education:
Ph.D., Computer Science, Harvard University

Research and Specialty:
Computer Vision, Digital Image Processing, Digital Camera

Course Introduction:
Computer Vision and Artificial Intelligence Applications: Optical Character Recognition (OCR), Automatic Number Plate Recognition (ANPR), object recognition for automated checkout lanes, 3D model building (photogrammetry), autonomous driving vehicle, smile shutter, computer guided brain surgery…
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Professor, Department of Electrical Engineering, NTU

Ph.D. Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, Urbana, Illinois, USA

Deep Learning, Data Science, Signal Processing in Wireless Communications, Systems Biology, FinTech, and Optimization Theory

Financial technology (Fintech) is a broad category that refers to the innovative use of technology in designing and delivering financial services and products. While many technology innovations play essential parts in revolutionizing financial services, this course focuses on deep learning (DL) and its applications in FinTech. Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts with a deep representation of many layers. It has been proven to be highly successful in predictive tasks for computer vision and natural language processing applications. In this course, we hope to demonstrate how DL can be applied to achieve superior predictive performance in FinTech applications.