Postgraduate Courses

This course will provide fundamental techniques for data analytics, including data collection, data extraction, data integration and data cleansing. The students will learn how to manage and optimize the analytics value chain, including collecting and extracting the suitable values, selecting the right data processing processes, integrating the data from various resources, data governance, security and privacy for Big Data applications.

Data mining has recently emerged as a major field of research and applications. Aimed at extracting useful and interesting knowledge from large data repositories such as databases and the Web, data mining integrates techniques from the fields of database, statistics and AI.

Big data systems, including Cloud Computing and parallel data processing frameworks, emerge as enabling technologies in managing and mining the massive amount of data across hundreds or even thousands of commodity servers in datacenters. This course exposes students to both the theory and hands-on experience of this new technology. The course will cover the following topics. (1) Basic concepts of Cloud Computing and production Cloud services; (2) MapReduce - the de facto datacenter-scale programming abstraction - and its open source implementation of Hadoop. (3) Apache Spark - a new generation parallel processing framework - and its infrastructure, programming model, cluster deployment, tuning and debugging, as well as a number of specialized data processing systems built on top of Spark.

This course will introduce mathematical formulations and computational methods (convex/non-convex optimization) to exploit structures contained in the data. Moreover, specific computational methods (Randomized computational methods) will be explored for big data analysis.

This course will introduce visualization techniques for data from everyday life, social media, business, scientific computing, medical imaging, etc. The topics include human visual system and perception, visual design principles, open- source visualization tools and systems, visualization techniques for CT/MRI data, computational fluid dynamics, graphs and networks, time-series data, text and documents, Twitter data, and spatio-temporal data.

Analysis of asset returns: autocorrelation, predictability and prediction. Volatility models: GARCH- type models, long range dependence. High frequency data analysis: transactions data, duration. Markov switching and threshold models. Multivariate time series: cointegration models and vector GARCH model.

The course will introduce basic techniques about optimization, including unconstrained optimization and constrained optimization, and matrix computation, including matrix analysis, linear systems, orthogonalization and least squares and eigenvalue problems.

This course is an introduction to social information network analysis and engineering. Students will learn both mathematical and programming knowledge for analyzing the structures and dynamics of typical social information networks (e.g. Facebook, Twitter, and MSN). They will also learn how social metrics can be used to improve computer system design as people are the networks. It will cover topics such as small world phenomenon; contagion tipping and influence in networks; models of network formation and evolution; the web graph and PageRank; social graphs and community detection; measuring centrality; greedy routing and navigations in networks; introduction to game theory and strategic behavior; social engineering; and principles of computer system design.

Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for tasks of interest.

This course will introduce the basic techniques for image data processing and analysis. Topics include image processing and analysis in spatial and frequency domains, image restoration and compression, image segmentation and registration, morphological image processing, representation and description, feature description, face recognition, iris recognition, fingerprint recognition, image analysis topics, such as medical image analysis.

This course introduces basic statistical principles, methodology and computational tools needed in performing data analysis. The topics of the course include parametric models, sufficiency principles, estimation methods, liner models, quantile estimations, nonparametric curve estimation, resampling methods, statistical computing and hypothesis testing.

The course introduces fundamentals of machine learning, including concept learning, evaluating hypotheses, supervised learning, unsupervised learning and reinforcement learning, Bayesian learning, ensemble methods.

This course will introduce statistical predication models and algorithms, including regression models, classification, additive models, graphical models and network, model assessment and selection, model inference and model averaging.

An independent project carried out under the supervision of a faculty member. This course may be repeated for credit.