NTU Course
Overview

Frontiers of Artificial Intelligence of Things

Offered in 112-2

  • Serial Number

    64462

  • Course Number

    Data5013

  • Course Identifier

    946 U0130

  • No Class

  • 3 Credits

  • Elective

    Data Science Degree Program / Taiwan International Graduate Program On Artificial Intelligence of Things

      Elective

    • Data Science Degree Program

    • Taiwan International Graduate Program On Artificial Intelligence of Things

  • CHIH-YU WANG
  • Tue 6, 7, 8

  • 新503

  • Type 2

  • 20 Student Quota

    NTU 20

  • No Specialization Program

  • English
  • NTU COOL
  • Core Capabilities and Curriculum Planning
  • Notes

    The course is conducted in English。The course schedule has been adjusted for certain weeks, kindly refer to the course syllabus for more information.

  • Limits on Course Adding / Dropping

    • Restriction: MA students and beyond

  • NTU Enrollment Status

    Enrolled
    0/20
    Other Depts
    0/0
    Remaining
    0
    Registered
    0
  • Course Description
    This course aims to serve as a guide to the frontier of Artificial Intelligence of Things (AIoT) research, development, and implementation. The course will cover four major areas of AIoT: Internet of Things, Embedded System, Artificial Intelligence, and Multimodal in a systematic way. For each area, we will invite the related professors/researchers to introduce the frontier of the area, including but not limited to the latest research, developments, and future trend. Students will be able to absorb new knowledge, exchange ideas, practice research skills, and do research works with professors/researchers on advanced topics.
  • Course Objective
    The structure of the course is designed to help the students begin their AIoT research with the support of the course resources and instructors. The course will be delivered through a series of lectures which are categorized into areas. For each area, each student should complete the area assignment, which will guard them to explore the topics covered in the lectures.
  • Course Requirement
    (Recommended) Prerequisite courses: Introduction of Artificial Intelligence of Things and its Applications
  • Expected weekly study hours before and/or after class
  • Office Hour
  • Designated Reading
    There is no official course textbook. Instead, each instructor will provide a list of reading materials (such as journal articles, conference papers, or selected chapters from an assigned textbook) before the lecture. Students are strongly recommended to read the assigned materials before the lecture to have a more meaningful and interactive learning experience.
  • References
    ● BALAS, Valentina E., et al. (ed.). Recent trends and advances in artificial intelligence and internet of things. Cham: Springer International Publishing, 2020. ● FORTINO, Giancarlo; TRUNFIO, Paolo (ed.). Internet of things based on smart objects: Technology, middleware and applications. Springer Science & Business Media, 2014. ● MANGLA, Monika, et al. (ed.). Real-life applications of the Internet of Things: challenges, applications, and Advances. 2022.
  • Grading
    10%

    Team project

    The team project will be treated as independent graduated-level research work as a practice for the students to gain research experience. Each team will be required to present the achievements of their project at the end of the semester and deliver the final report. Grading of the project will be based on the quality, originality and creativity of the work, as well as the clarity and professionalism of the presentation and the report. Plagiarism will result in a failing grade for the assignment and may result in further disciplinary action.

    90%

    Class participation / Assignments

    Class participation includes attendance, participation in (onsite/online) discussions, and group activities. The area assignments could be a survey, a personal project, a take-home exam, or others. The exact form will be determined by the instructors. Grading of the assignments will be based on the completeness of the assignment goal.

    1. NTU has not set an upper limit on the percentage of A+ grades.
    2. NTU uses a letter grade system for assessment. The grade percentage ranges and the single-subject grade conversion table in the NATIONAL TAIWAN UNIVERSITY Regulations Governing Academic Grading are for reference only. Instructors may adjust the percentage ranges according to the grade definitions. For more information, see the Assessment for Learning Section。
  • Adjustment methods for students
  • Make-up Class Information
  • Course Schedule
    2/20Week 1Course Introduction | Prof. Chih-Yu Wang
    2/27Week 2Rescheduled to 2/29(Thur.) 09:10-12:20 新502 Visible Light Communications and Positioning | Prof. Hsin-Mu Tsai
    3/05Week 3Brain-Computer Interface and its applications | Prof. Yu-Te Wang
    3/12Week 4Pragmatic Natural Language Processing | Prof. Hen-Hsen Huang
    3/19Week 5Large Language Model | Prof. Lun-Wei Ku
    3/26Week 6Rescheduled to 3/27(Wed.) 14:20-17:20 新503 Smart Healthcare: How can AI Revolutionize the Healthcare Ecosystem | Prof. Che Lin
    4/02Week 7TBD | Prof. Jun-Cheng Chen
    4/09Week 8Utilizing Deep Learning for Speech Enhancement in Assistive Oral Communication Technologies | Prof. Yu Tsao
    4/16Week 9Retrieval Augmented Generation: The Retriever | Chih-Ming Chen (Postdoc)
    4/23Week 10TBD | Prof. Ti-Rong Wu
    4/30Week 11Introduction to Foundation Model | Prof. Hung-Yi Lee
    5/07Week 12Some Basics in Streaming Model | Prof. Meng-Tsung Tsai
    5/14Week 13Memory-Centric Computing | Prof. Hsiang-Yun Cheng
    5/21Week 14SIoT with MR | Prof. De-Nian Yang
    5/28Week 15TBD | Prof. Jen-Chun Lin
    6/4Week 16Project Presentation | Prof. Chih-Yu Wang