Computer Science

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.   

This course aims to guide students to understand the core concepts, principles, and technologies of artificial intelligence, experience the cutting-edge applications of artificial intelligence technology in various fields, and cultivate students' thinking and practice of artificial intelligence technology. Through course learning, students can initially grasp the basic knowledge, core technologies, and algorithms of artificial intelligence, understand the current development direction of artificial intelligence technology, and use AI tools and thinking to solve practical problems in their own fields.

This course introduces the basics of Perl scripting as well as handling of bioinformatic data. Upon completion of the course, students should be familiar with basic usage of LINUX systems and experienced with Perl scripting. Students should be able to code basic scripts, handle external files, design data structure, execute regular expression tests, hash and array usage, use modules, create Perl subroutines and much more. The course features lectures and hands-on exercises every week.  

The course is a practical programming class focused on artificial intelligence (AI) examples. The course discusses introductory Python language at the beginning; engages in hands-on programming in class and implements AI examples in the final month of the course. The course covers basic to advanced concepts of the Python programming language. The examples and exercises provided in the course primarily emphasize AI applications. Finally, students will utilize Python to implement the final project, which involves programming tasks and a final presentation.  

The purpose of the course is that the students should learn how to write efficient programs in the C language. In order to achieve this main purpose, three other purposes of the course are that the students should learn about (1) the ISO C18 language, (2) modern computer architecture, from the perspective of the programmer, with focus on microprocessors and cache memories, and (3) modern tools to evaluate C programs in terms of correctness and efficiency.

This course examines the data structures and algorithms useful in programming competitions and technical interviews. Topics include but are not limited to advanced data structures, advanced search algorithms, decomposition techniques, combinational games, basic computational geometry, and basic number theory.

This course covers methods and techniques to summarize and communicate the underlying patterns of different types of data. Students practice preprocessing (Python) and various data visualization tools, working on projects with real-world data. Topics include overview of data visualization, graph visual perception and design principles, data types and visualization techniques, basic and advanced charts and graphs, effective communication, time series data visualization and interactive visualizations, statistical visualizations, network data, machine learning, and data visualization trends.  

This course introduces mathematical tools to evaluate software products and algorithms, examining principal knowledge of data structures in order to represent real world problems with computational algorithms (with abstract data types).  

Topics include linear lists, queues, trees, arrays, and hash tables. The course also investigates the principles of software engineering and addresses how to best utilize the acquired principles in designing and realizing software problems through hands-on experience.