Computer Science

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. 

This course examines Big Data computing systems and programming models. It covers the architecture and components of Hadoop and Spark, data processing with Spark, and advanced topics such as Spark Streaming, graph processing, and machine learning. Students will learn to develop operational and programming tools for data collection, serialization, migration, and workflow coordination in Big Data pipelines. 

This course cultivates a deep understanding of data augmentation techniques and robust machine learning principles and the ability to apply them to real-world problems.  

Students will implement various data augmentation techniques using programming languages and machine learning libraries and develop problem-solving skills to diagnose and address the performance degradation caused by noisy labels and imbalanced data. Additionally, students will master the use of cross-validation and performance metrics to effectively evaluate models, and learn methods to interpret and explain model predictions, ensuring the development of transparent and trustworthy machine learning applications. The course also emphasizes the ethical aspects of data augmentation and robust machine learning, fostering the ability to implement ethical practices that ensure responsible use of technology. Students will nurture a research-oriented mindset and enhance their collaboration skills through team projects and group discussions, promoting the exchange of ideas.