Hong Kong

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 examines the essential areas in biomedical engineering, including technologies and applications in life sciences and medicine. The course is broadly divided into 4 areas: biomechanics and biomaterial; cell and tissue engineering; biomedical instrumentations and signals, and medical imaging. The global development and other issues, such as safety, ethics and industry will also be addressed.

This course examines various environmental challenges in contemporary societies from a sociological perspective. Recognizing that environmental problems are often intricately connected with the conditions of societies that they are situated in, it explores the processes underlying social and environmental changes as well as the consequences that those processes may entail at national, regional, and global levels. Substantive topics to be covered include limits of growth and development, sustainable production and consumption, climate change and global governance, and environmental movement.

This course examines the principles and techniques related to the formation, dispersion and control of various air pollutants formed from anthropogenic pollution sources. Topics include: micrometeorology; air dispersion; combustion fundamentals; pollutant formation mechanism and control technologies; abatement of volatile organic compounds using incineration techniques; particulate and aerosol abatement technology; particle technology, log-normal distribution; settling chamber; cyclone; electrostatic precipitator; and bag filter. 

This course examines fundamental concepts in probability and mathematical statistics, including probabilistic modelling, limiting results, estimation and hypothesis testing. Topics include random variables and vectors, distribution and quantile functions, covariance and correlation matri-ces, strong law of large numbers, central limit theorem, estimators and their (asymptotic) properties, parametric estimators (maximum likelihood, method of moments), (asymptotic) conĄdence intervals (mean and variance of a normal, difference of means of two normals, ratio of means of two normals), hypothesis tests (theory, power function, p-value, asymptotic tests, likelihood ratio tests). 

This is a historical and critical survey of modern Chinese fiction from 1917 to 1949, with emphasis on the forms of novella and short story. 

This course examines philosophical issues concerning knowledge and the mind. These include metaphysical questions about what minds are, such as whether the mind is something non-physical, and questions about what knowledge is and how (and whether) we can obtain it. We will also cover questions about the existence of god, the possibility of free will, and personal identity.

The analysis of variability is mainly concerned with locating the sources of the variability. Many statistical techniques investigate these sources through the use of linear models. This course presents the theory and practice of these models. Topics include: simple linear regression: least squares method, analysis of variance, coefficient of determination, hypothesis tests and confidence intervals for regression parameters, prediction; multiple linear regression: least squares method, analysis of variance, coefficient of determination, reduced versus full models, hypothesis tests and confidence intervals for regression parameters, prediction, polynomial regression; one-way classification models: one-way ANOVA, analysis of treatment effects, contrasts; two-way classification models: interactions, two-way ANOVA for balanced data structures, analysis of treatment effects, contrasts, randomized complete block design; universal approach to linear modeling: dummy variables, multiple linear regression representation of one-way and two-way (unbalanced) models, ANCOVA models, concomitant variables; regression diagnostics: leverage, residual plot, normal probability plot, outlier, studentized residual, influential observation, Cook's distance, multicollinearity, model transformation. 

This course examines American hemispheric history, society, and culture across North and South America. The course will introduce significant social and cultural developments in selected countries of the Americas. Topics will include indigenous - colonial relations; slavery and its legacies; the impact of modernity on society and culture; the struggle for civil rights in 20th and 21st centuries; wars and empire; immigration, forced migration and its impact on politics; globalization and neoliberal economics; and the rise of populist nationalism in the 21st century.