Computer Science

This course offers a study of bioinformatics methodologies. Topics include: introduction to programming; processing and analysis of biological sequence data; modeling and simulation of molecular dynamics; genomics and metagenomics; transcriptomics.

This course focuses on computation in the nervous system. Students are introduced to basic neuroscience concepts, learn about how computational models are used to simulate processes in the brain, and learn about theories for how the brain processes information and performs computations. Topics include an introduction to basic neuroscience concepts, models of neurons, neural encoding, neural decoding, information theory, network models, and plasticity/learning. The course is delivered through lectures and computer labs.

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.