Computer Science

This course covers the foundations and growing applications of Web computing, ranging from web crawling, search, and mining to recent trends in natural language processing on the Web. The course is designed to help students understand the fundamental notions and software technologies underlying Web information services. Topics covered: frontiers in web computing; natural language processing for text processing; foundations of information retrieval; advances in information retrieval; information extraction from documents; from information extraction to knowledge acquisition; social network analysis and recommendation systems, and web service mashups. 

This course introduces students to several computer intensive statistical methods and the topics include: empirical distribution and plug-in principle, general algorithm of bootstrap method, bootstrap estimates of standard deviation and bias, jack-knife method, bootstrap confidence intervals, the empirical likelihood for the mean and parameters defined by simple estimating function, Wilks theorem, and EL confidence intervals, missing data, EM algorithm, and Markov Chain Monte Carlo methods. This course has a prerequisite of Mathematical Statistics. 

In this course, students learn how images are formed, how they are represented on computers, and how they can be processed by computers to extract semantic information. Students develop algorithms for detecting interesting features in images, design neural networks to perform natural image classification, and explore algorithms for solving real-world problems such as hand-written digit recognition and object detection.  

This course examines various aspects of data processing including database management, representation and analysis of data, information retrieval, visualization and reporting, and cloud computing. 

This course enables students with no technical background to have a general understanding and a taste of hands-on exploration of Artificial Intelligence (AI) and Machine Learning (ML). The course covers the basic concepts, problems, approaches and applications of AI components and systems. It provides an introduction to various topics in AI systems and technologies, e.g., an overview of AI, data representation and visualization, basics of ML, ethical and legal issues with AI, etc. It discusses the applications of engineering principles to selected AI and ML problems, including image classification, machine translation, and voice cloning. It also explores the future possibilities and challenges of AI.

This course examines a range of machine learning tools and techniques for analyzing data and automatically generating applications. The course will address tools for classification, regression, clustering and text mining, and techniques for preprocessing data and analyzing the results of machine learning tools. 

In many projects, it is important for programmers to have fine control over low-level details of program execution, and to be able to assess the cost of a design decision on likely overall program performance. This course introduces students to a system programming language that gives programmers this kind of control, explores a range of standard data structures and algorithmic techniques, and shows how to apply them to frequently encountered problems.

This course focuses on the theory of linear models. Topics include: linear regression model, general linear model, prediction problems, sensitivity analysis, analysis of incomplete data, robust regression, multiple comparisons, and an introduction to generalized linear models. This course has a prerequisite of Regression Analysis.

In this course we will examine how existing network layers are protected, how to verify the security of a protocol, and how to improve the dependability. We will specifically learn about the common vulnerabilities in the current Internet, such as botnets, viruses, denial-of-service attacks, etc., and design principles to overcome these issues in the future. We will also learn about the security benefits and challenges of network virtualization technologies, about air-gapping, as well as automated network testing methods and fuzzing.

Students work in a multidisciplinary team to develop a game or graphics system up to release quality. The course brings together practical development and theoretical analysis to ensure students know both how to make games and how to assess them.