NTU Course
Overview

Special Topics on Artificial Intelligence and Cybersecurity

Offered in 112-2Updated

  • Serial Number

    18346

  • Course Number

    CommE7005

  • Course Identifier

    942 M0320

  • No Class

  • 3 Credits

  • Compulsory

    GRADUATE INSTITUTE OF COMMUNICATION ENGINEERING

      Compulsory

    • GRADUATE INSTITUTE OF COMMUNICATION ENGINEERING

  • HSI-TSENG CHOU
    • View Courses Offered by Instructor

    • COLLEGE OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE GRADUATE INSTITUTE OF COMMUNICATION ENGINEERING

    • chouht@ntu.edu.tw

    • 電資學院電二館4樓441 室

    • 02-33669646

  • Mon 9, 10, A

  • Please contact the department office for more information

  • Type 2

  • 7 Student Quota

    NTU 7

  • No Specialization Program

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

    Not open in course pre-registration period。 The course is conducted in English。、 RUEY-BEEI WU、 WAN-JIUN LIAO、 TSUNG-NAN LIN、 HUNG-YU WEI、 SHAU-GANG MAO、 PEI-YUAN WU、 I-CHI LAI、 CHE LIN、 Chang, Ji-en、 SEE-MAY PHOONG、 TIAN-WEI HUANG、 HSUAN-J

  • NTU Enrollment Status

    Enrolled
    0/7
    Other Depts
    0/0
    Remaining
    0
    Registered
    0
  • Course Description
    This course addresses privacy-preserving techniques in machine learning, including, Differential privacy techniques, Privacy for frequency or mean estimation, Naive Bayes classifier, and deep learning, Compressive privacy for machine learning, Privacy-preserving synthetic data generation approaches and Privacy-enhancing technologies for data mining and database applications.
  • Course Objective
    Understanding of basic issues, concepts, principles, and mechanisms in privacy-preserving techniques in machine learning. Be able to determine appropriate mechanisms to protect privacy in various machine learning algorithms. Understanding of the latest research results in privacy preservation techniques.
  • Course Requirement
    It is highly desirable that you have successfully finished introductory computer programming courses. Prior knowledge of machine learning fundamentals is recommended.
  • Expected weekly study hours before and/or after class
  • Office Hour
  • Designated Reading
  • References
    J. Morris Chang, Di Zhuang and G. Dumindu Samaraweera, “Privacy-Preserving Machine Learning”, Manning Publications, 2023 (ISBN: 9781617298042) https://www.manning.com/books/privacy-preserving-machine-learning
  • Grading
    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