Capabilities \ Core Courses	A. To cultivate students with a foundational understanding of data science in an interdisciplinary field. B. To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration. C. To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions. D. To understand the impact of their research on the living environment and international society. E. To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
	[A]	[B]	[C]	[D]	[E]
Advanced Statistical Inference (Ⅰ)	◎	◎	◎	◎	◎
Advanced Statistical Inference (Ⅱ)	◎	◎	◎	◎	◎
Probability Theory (Ⅰ)	◎	◎	◎	◎	◎
Probability Theory (Ⅱ)	◎	◎	◎	◎	◎
Numerical Linear Algebra	◎	◎	◎	◎	◎
Mathematics in Signal Processing and Machine Learning	◎	◎	◎	◎	◎
AI for Medical Image Analysis	◎	◎	◎	◎	◎
Mathematics in Deep Learning	◎	◎	◎	◎	◎
The practical implementation of artificial intelligence in medical images	◎	◎	◎	◎	◎
Regression Analysis	◎	◎	◎	◎	◎
Capabilities \ Core Courses	A. To cultivate students with a foundational understanding of data science in an interdisciplinary field. B. To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration. C. To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions. D. To understand the impact of their research on the living environment and international society. E. To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
	[A]	[B]	[C]	[D]	[E]
Statistical Computing	◎	◎	◎	◎	◎
Multivariate Statistical Analysis	◎	◎	◎	◎	◎
Statistical Learning	◎	◎	◎	◎	◎
Bayesian Statistical Methods	◎	◎	◎	◎	◎
Computational Social Science	◎	◎	◎	◎	◎
Biomedical Statistics	◎	◎	◎	◎	◎
Computer Communication Networks	◎	◎	◎	◎	◎
Data Science	◎	◎	◎	◎	◎
Advanced Algorithms	◎	◎	◎	◎	◎
Machine Learning	◎	◎	◎	◎	◎
Capabilities \ Core Courses	A. To cultivate students with a foundational understanding of data science in an interdisciplinary field. B. To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration. C. To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions. D. To understand the impact of their research on the living environment and international society. E. To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
	[A]	[B]	[C]	[D]	[E]
Medical Imaging Systems	◎	◎	◎	◎	◎
Introduction to Generative Artificial Intelligence	◎	◎	◎	◎	◎
Advanced Artificial Intelligence	◎	◎	◎	◎	◎
Bioinformatics	◎	◎	◎	◎	◎
Natural Language Processing	◎	◎	◎	◎	◎
Machine Learning	◎	◎	◎	◎	◎
Robotics	◎	◎	◎	◎	◎
Information Retrieval and Extraction	◎	◎	◎	◎	◎
Artificial Intelligence	◎	◎	◎	◎	◎
Cryptography and Network Security	◎	◎	◎	◎	◎
Capabilities \ Core Courses	A. To cultivate students with a foundational understanding of data science in an interdisciplinary field. B. To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration. C. To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions. D. To understand the impact of their research on the living environment and international society. E. To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
	[A]	[B]	[C]	[D]	[E]
Medical Image Processing	◎	◎	◎	◎	◎
Topics in Machine Learning	◎	◎	◎	◎	◎
Big Data Systems	◎	◎	◎	◎	◎
Application of Big Data System	◎	◎	◎	◎	◎
Applied Deep Learning	◎	◎	◎	◎	◎
Advanced Deep Learning	◎	◎	◎	◎	◎
Distributed Machine-Learning System	◎	◎	◎	◎	◎
Optimization Algorithms	◎	◎	◎	◎	◎
High-Performance Big Data and Artificial Intelligence Systems	◎	◎	◎	◎	◎
Security and Privacy of Machine Learning	◎	◎	◎	◎	◎
Capabilities \ Core Courses	A. To cultivate students with a foundational understanding of data science in an interdisciplinary field. B. To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration. C. To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions. D. To understand the impact of their research on the living environment and international society. E. To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
	[A]	[B]	[C]	[D]	[E]
Deep Learning for Medical Imaging	◎	◎	◎	◎	◎
3D Computer Vision with Deep Learning Applications	◎	◎	◎	◎	◎
Online Convex Optimization	◎	◎	◎	◎	◎
Computer Vision Practice with Deep Learning	◎	◎	◎	◎	◎
Digital, AI and Emerging Computing Architecture	◎	◎	◎	◎	◎
Trustworthy Artificial Intelligence	◎	◎	◎	◎	◎
Convex Optimization	◎	◎	◎	◎	◎
Deep Learning for Computer Vision	◎	◎	◎	◎	◎
Deep Learning for Human Language Processing	◎	◎	◎	◎	◎
Artificial Intelligence and Intelligent Medicine	◎	◎	◎	◎	◎
Capabilities \ Core Courses	A. To cultivate students with a foundational understanding of data science in an interdisciplinary field. B. To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration. C. To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions. D. To understand the impact of their research on the living environment and international society. E. To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
	[A]	[B]	[C]	[D]	[E]
Deep Learning for Music Analysis and Generation	◎	◎	◎	◎	◎
Master Thesis	◎	◎	◎	◎	◎
Special Project	◎	◎	◎	◎	◎
Seminar	◎	◎	◎	◎	◎
Advanced Methods in Optimization Algorithms	◎	◎	◎	◎	◎
Mobile Cloud Service APP Design	◎	◎	◎	◎	◎
Statistical Foundations of Data Science (I)	◎	◎	◎	◎	◎
Nonlinear Optimization: Theory and Applications	◎	◎	◎	◎	◎
Computer Programming in Python	◎	◎	◎	◎	◎
Data Analysis and Manifold Learning	◎	◎	◎	◎	◎
Capabilities \ Core Courses	A. To cultivate students with a foundational understanding of data science in an interdisciplinary field. B. To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration. C. To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions. D. To understand the impact of their research on the living environment and international society. E. To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
	[A]	[B]	[C]	[D]	[E]
Data Analysis and Manifold Learning	◎	◎	◎	◎	◎
Computational Methods for Data Science	◎	◎	◎	◎	◎
Computational Methods for Data Science	◎	◎	◎	◎	◎
Introduction to Artificial Intelligence of Things and its Applications	◎	◎	◎	◎	◎
Introduction to Social Networks	◎	◎	◎	◎	◎
Frontiers of Artificial Intelligence of Things	◎	◎	◎	◎	◎
NLP and Information Retrieval with Applications in Social Networks	◎	◎	◎	◎	◎
Core Capabilities	A To cultivate students with a foundational understanding of data science in an interdisciplinary field.	B To train students in integrating information, identifying problems, analyzing strategies, conducting research, and build up the capabilities of system intergration.	C To develop students' ability to think critically, analyze problems, and propose innovative research ideas or solutions.	D To understand the impact of their research on the living environment and international society.	E To understand professional academic ethics, value intellectual property rights, and continuously grow in professional domain knowledge.
Number of Core Courses （Number of All Courses: 67）	67	67	67	67	67