2018-2 Semester

Graduate Course - Bioinformatics Practice(BIT502)

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, biology, mathematics, statistics and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. More broadly, bioinformatics is applied statistics and computing to biological science.

Under-graduate Course - Machine Learning(COSE362)

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.

2018-1 Semester

Graduate Course - Introduction to Bioinformatics(BIT501)

Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, biology, mathematics, statistics and engineering to analyze and interpret biological data. Bioinformatics has been used for in silico analyses of biological queries using mathematical and statistical techniques. More broadly, bioinformatics is applied statistics and computing to biological science.

Under-graduate Course - Data Science(COSE471)

Data science is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining. This course aims to give students broad understanding of big data analytics. Topics covered include big data processing and visualization, and machine learning techniques. Please notice that this course is designed as 8 weeks course. The course starts from the first week of May. Each week, we will meet three times and each class will last 1 hour and 40 minutes. The first class will begin on May 1.