Computer Science

The first part of this course covers fundamental topics in discrete mathematics that underlie many areas of computer science and presents standard mathematical reasoning and proof techniques such as proof by induction. The second part of this course covers discrete and continuous probability theory, including standard definitions and commonly used distributions and their applications.

Through the teaching of this course, students are introduced to the basic ideas and methods of object-oriented programming. Through learning and combining computer practice, students can understand the general methods of computer problem solving, master interface production, program writing and debugging, and enable students to have the ability to write simple application programs. This will lay a solid foundation for further self-study of other programming languages and improve programming skills in the future, while also improving students' logical thinking ability and meticulous thinking ability.

The basic contents include: VB development environment and basic programming steps, basic controls and language basics, basic control structures, arrays, procedures, commonly used controls, menus, multiple forms and files, etc.

This course provides instruction in building cutting-edge interactive systems and guides to design futuristic experiences. Students gather in Make Reality Space in a studio format to construct software and hardware prototypes. Topics for each semester may change and evolve towards ultimate reality. 
 
This course focuses on mixed reality technologies. Specifically, students use Meta Quest 3, Intel Realsense cameras, computer vision toolkits, 3D printed props, and Unity game engines to connect both the physical and virtual worlds. In groups, students design and build their own interactive hardware/software prototypes and present them in a live demo at the end of term.   

This course introduces students to the concepts and methodology needed to implement and analyze computational models of cognition. It considers the fundamental issues of using a computational approach to explore and model cognition. In particular, this course explores the way that computational models relate to, are tested against, and illuminate psychological theories and data. The course introduces both symbolic and subsymbolic modelling methodologies, and provides practical experience with implementing models. The symbolic part focuses on cognitive architectures, while the subsymbolic part introduces probabilistic models. 

Data processing is an important aspect for every field of study, and Excel is one of the most commonly used methods. Through lecture and homework exercises, this course aims to introduce basic and advanced functions of Excel that are capable of meeting most data processing needs.   

Artificial intelligence in medicine has created tremendous business opportunities recently, creating an ideal environment for AI-Biomedical interdisciplinary specialists to make considerable contributions and significantly impact the world. Intelligent medicine aims to utilize state-of-the-art AI technologies for many medical applications such as accurate disease risk prediction and essential predictors selection, which are for early precise and efficient treatments. This course introduces the vast potential of intelligent medicine, seeking to advance student skills and motivation for AI-Biomedical interdisciplinary science.  

The course also introduces potential partners for future interdisciplinary collaboration to our students and provide opportunities for practical implementations through several carefully designed experiments, which shall demonstrate how to leverage real-world medical resources and related AI technologies. The course includes visits to prestigious companies and institutes and as well as seminars. 

This course examines supervised and unsupervised learning, with emphases on the theoretical underpinnings and on applications in the statistical programming environment R. Topics include linear methods for regression and classification, model selection, model averaging, basic expansions and regularization, kernel smoothing methods, additive models and tree-based methods. We will also provide an overview of neural networks and random forests.

This course introduces common methods on climate/weather data processing via Python. It aims to guide students on how to get the information from climate/weather datasets by data visualization and instructs on how to use this information to finalize their own narrative. 
 
The course consists of three stages: 
First stage: Introduction and Basic Syntax of Python 
Second stage: Reproduce/Rewrite some exiting codes 
Third stage: Course Review & Final report (individual)

This course offers practical training in data science, focusing on high-dimensional data computing and dimension reduction algorithms. The characteristics of this course are the hands-on experience with high-performance computers and the observation of real data from a statistical perspective. Practical exercises will be conducted on high performance GPU servers on the cloud, possibly utilizing resources such as the NVIDIA V100 from our NTU or Google Colab. In addition to the hands-on exercises, statistical theories related to dimension reduction algorithms, data visualization, and data interpretation are introduced. The Python programming skills are taught during the first month as part of a combined and quick recap course. The course is taught in English, but bilingual Q&A sessions are acceptable. 

This course discusses the basic principles and practical applications of bioinformatics. The course discusses the processing power of computers to effectively solve data analysis in biomedical research, the application analysis of biomedical databases, and biomolecular structure analysis and functions prediction.