2. Curriculum Design

Course Design Principles

The goal of establishing the “Master’s Program in Artificial Intelligence and Innovative Applications” in the Department of Computer Science and Information Engineering is to cultivate multidisciplinary talents capable of applying artificial intelligence technology to innovative cross-domain applications. The course design principles are as follows:

  • Establishment of foundational knowledge in artificial intelligence: Offer courses in mathematical theory and programming relevant to artificial intelligence to build the foundational skills necessary for students from different professional backgrounds to learn about artificial intelligence and develop cross-domain application capabilities.
  • Learning of core artificial intelligence technologies: Provide courses in core artificial intelligence technologies to lay the theoretical foundation and cultivate practical skills, aiming to equip students with expertise in core artificial intelligence technologies as a professional basis for practical applications.
  • Training in cross-domain application capabilities of artificial intelligence: Offer courses in cross-domain applications of intelligent technology to deepen and broaden students’ understanding and cultivation of artificial intelligence expertise through practical applications. This expands students’ diverse knowledge and abilities, enhancing their competitiveness in career development.
  • Development of creative research and development capabilities in artificial intelligence: Offer training courses in innovative research and development to train students in analytical thinking, creativity, hands-on practice, and problem-solving abilities, enabling them to execute creative research and development projects.
  • Course Structure Design

Based on the above principles, the master’s program is structured into four major course clusters: “AI Foundation Skills” cluster, “AI Core Technologies” cluster, “AI Cross-Domain Projects” cluster, and “Creative Research Training” cluster. The content and goals of each cluster are explained as follows:

  1. AI Foundation Skills Cluster: This cluster includes courses in machine learning and artificial intelligence.
  2. AI Core Technologies Cluster: This cluster covers a wide range of core professional courses, including deep learning, big data analysis, data mining, digital signal processing, image processing, computer vision, audio processing and analysis, natural language processing, human-computer interaction, information security, smart Internet of Things (IoT), mobile computing, data visualization, virtual reality and augmented reality, embedded intelligent system design, unmanned vehicle implementation and application, sensor device principles, and others. The aim of this cluster is to establish students’ theoretical and practical abilities in core artificial intelligence technologies, serving as a professional foundation for taking cross-domain application courses and conducting master’s thesis research.
  3. AI Cross-Domain Projects Cluster: Based on the training goal of equipping students with the ability to integrate artificial intelligence technology and cross-domain applications, this cluster will design several application areas. Students will select and complete specialized courses in their chosen area of interest and thesis research topic. The cluster emphasizes in-depth professional training, with instructors designing relevant issues in the field to guide students through domain background exploration, problem analysis, and the application of artificial intelligence technology to problem-solving. Currently, our teaching team includes several professors who have executed cross-domain collaborative projects commissioned by the Ministry of Science and Technology and industry. Additionally, collaborations with faculty from various interdisciplinary departments within our university are sufficient to meet the instructional needs of this cluster.
    1. Smart Agriculture and Food Technology Area Project
    2. Smart Tourism and Leisure Area Project
    3. Smart Healthcare Area Project
    4. Smart Learning Science Area Project
    5. Smart Counseling and Guidance Area Project
    6. Smart Cultural and Artistic Heritage Area Project
    7. Smart Environmental Sustainability Area Project
  4. Creative Research Training Cluster: This cluster requires students to take courses in “Thesis Seminar” and “Master’s Thesis” to train them in observing and critiquing research papers, applying creative problem-solving skills, and writing scientific papers and technical reports.

Academic Regulations

  1. Master’s degree students must take professional elective courses, including at least 6 credits from the “Artificial Intelligence Course Category 5 out of 2”; they must take at least 9 credits from professional elective courses offered by the Master’s program, and the remaining 9 credits can be selected from other graduate programs’ professional elective courses.
  2. Master’s students may select any Master’s and Ph.D. level courses to study; the course credits can be counted toward graduation credits.
  3. The maximum credit transfer for professional elective courses is half of the credits. Courses offered by our department are not subject to this limit. Credit transfer procedures follow the university’s regulations.
  4. Master’s students must take the “Special Topics Seminar” course every semester until completion or graduation. However, credits from this course, except for (1) and (2), are not counted towards graduation credits. Refer to the specific regulations for five-year study programs for guidance.
  5. Master’s students must take the “Thesis Research” course every semester, with a maximum of 2 instances counting for 4 credits each towards graduation.
  6. Master’s students must pass English proficiency exams to graduate. Refer to the “English Proficiency Graduation Regulations for the Department of Computer Science and Engineering Graduate Institute” for details.
  7. All students (including double majors) must take the “Programming Ability Test” course and achieve a passing grade. However, the grade obtained does not affect graduation.
  8. Scholarship recipients must comply with additional course requirements and academic completion criteria according to the scholarship regulations.
  9. Starting from this academic year, graduate students in our department must register for the “Academic Research Ethics Education Course” on the Taiwan Academic Ethics Education Resource Center’s online platform during the first semester after enrollment. They must pass the online course test and provide proof of completion to apply for degree examinations. Failure to pass requires completion of the course before applying for degree examinations.

Course Listings

Course Title(English) Credit *Prerequisite course or #Background course Remarks
Compulsory (6 credits)
Thesis 2.0 Must take every semester
Special Topic Lecture (Ⅰ) 1.0
Special Topic Lecture (Ⅱ) 1.0
Elective
Image Processing 3.0
Advanced Computer Vision 3.0
Realization of Soft Computing Systems 3.0
Advanced Information Retrieval 3.0
Big Data Analytics 3.0
Pattern Recognition 3.0
Intelligent System Design 3.0
Speech Processing and Recognition 3.0
Big Data Systems 3.0
Optimization methods and applications 3.0
Machine Learning in Computer Vision 3.0
Recommender System 3.0
Intelligent Resource Management for Heterogeneous Wireless Networks 3.0
Topics on optimization and decision making 3.0
Intelligent digital learning 3.0
Soft Computing 3.0
Programming Ability Certification 0.0
Intelligent IoT technologies and applications 3.0
Social Networks and recommender systems 3.0
Pragmatic Programming and Applications 3.0
Clinical Medicine and Smart Healthcare 3.0
Highly interactive multimedia design 3.0
Theory of Computer Games 3.0
/ 3.0
Special Topic Lecture (Ⅲ) 1.0
Special Topic Lecture (Ⅳ) 1.0
Science and Technical Writing 3.0
Select 2 lectures from 5 lectures, at least 6.0 credits.
Artificial Intelligence 3.0
Machine Learning 3.0
Data Mining 3.0
Foundation and Practice of Deep Learning 3.0
Advanced Machine Learning Principles and Technology 3.0

 

(Visited 34 times, 1 visits today)