Computer Science

This course offers a study of data integration and visualization. Topics include: data integration models; data acquisition; noSQL databases in data integration; situational awareness and interpretation in the Big Data era; visual analytics; principles of human-machine interaction; visual interfaces; temporal and geo-spatial data processing; applications of visual analytics.

This course examines the skills and techniques to effectively manage digital products from cradle to grave. It covers the core aspects of digital product management, from product strategy, planning and development, to product launch and support. The course address issues of managing an evolving digital product over its life cycle and using data from customer insights and competitive analysis for ongoing product iterations. Case studies and hands-on experience are provided. At the end of the course, students are able to effectively execute the product manager’s role in managing digital products.

This course offers an introduction to data science for genomic data. It discusses basic algorithms for genome sequencing, compares DNA sequences or proteins, and analyzes databases with the genomic profiles of different patients for the extraction of information. Other topics include: computational methods for analyzing and performing DNA data sequencing; advanced statistical methods for the analysis of genomic data; statistical tests for the extraction of conclusions.

This course includes practical application of data science theory and methodology to real world issues. Students complete a data science project with the following content: data collection and pre-processing; development of a technical solution based on data science; analysis of legal and ethical aspects; analysis of economic feasibility of the proposed solution.

The course covers cryptology, web applications security, server security, client security, remote login, Email and spam, and DNS Security. A selection of the following topics is also included: E-cash overview: Blind signatures, blockchain technologies and digital monies, Bitcoin, Ethereum, smart contracts; Secure communication: TLS attacks; Database security: Inference, differential privacy; and anonymity: traffic analysis, Chaum's Mix, Tor.

This course covers processing principles of data types other than text format that are used to create multimedia contents like three dimensional solid body with various tools for the multimedia. 

The course covers the following topics:

• 3DCG
• Stereoscopic 3D
• 360degree video
• Virtual Reality
• Augmented Reality

The course introduces the basic concepts in search and knowledge representation as well as several sub-areas of artificial intelligence. It focuses on covering the essential concepts in AI. The course covers Turing test, blind search, iterative deepening, production systems, heuristic search, A* algorithm, minimax and alpha-beta procedures, predicate and first-order logic, resolution refutation, non-monotonic reasoning, assumption-based truth maintenance systems, inheritance hierarchies, the frame problem, certainly factors, Bayes' rule, frames and semantic nets, planning, learning, natural language, vision, and expert systems and LISP.

This course establishes the foundation of a wide range of statistical learning methods. It aims to understand and utilize the fundamentals of various statistical learning models. 

The course covers:

• statistical learning;
• classical linear methods for regression and classification; 
• cross-validation;
• bootstrap; 
• modern linear methods;
• nonlinear methods; 
• tree-based methods;
• support vector machines;
• unsupervised learning;
• neural networks, and
• deep learning. 

These topics are the basics of statistical learning, but the core of machine learning. By the end of this course, students will have easier access to and understanding of deep learning and artificial intelligence.

The course requires the following prerequisites: 

• Python Basic – this course assumes a basic knowledge of Python
• STAT 241: Matrix Theory or Linear Algebra - provides a computational foundation for understanding statistical models.
• STAT 232: Mathematical Statistics- knowledge of probability theory and asymptotic evaluations. 

This course introduces information technologies (IT) in organizations and the interplay between IT, work, management, and organizations. The course examines the impacts of modern IT and the related artificial intelligence (AI) technologies on knowledge workers, teamwork, work design, management practices, and the organization. The course discusses the multifaceted roles IT can play to support communication, collaboration, and organizational improvements in operations, planning, and decision making. Students learn to apply strategic thinking to identify opportunities for IT-enabled innovations and issues involving information systems (IS) adoption and deployment.