Computer Science

90% of security incidents exploit software vulnerabilities. This course studies principles and mechanisms for improving software security. It discusses various attack techniques; how to defend against them; and more importantly, how to develop software with less vulnerabilities in advance.

The course covers topics such as security principles, buffer overflow, race condition attacks and security development lifecycles (SDL), security by design. Operating systems and computer network courses are recommended before taking this course.

This course examines the ambivalent role of digital technologies in our societies and questions our future by questioning their relevance. It first considers where we come from and how the pre-web world prepared us for this new reality, notably through science fiction. It then invites us to understand what is happening in our daily lives by deciphering the announced technological advances and their effects on reality. Finally, the course imagines a horizon that seems most desirable for all.

This course is concerned primarily with computer graphics systems and in particular 3D computer graphics. It includes revision of fundamental raster algorithms such as polygon filling, and quickly moves onto the specification, modelling, and rendering of 3D scenes. The following topics may be covered: viewing in 2D, data structures for the representation of 3D polyhedra, viewing in 3D, visibility and hidden surface algorithms, illumination computations. Some attention will be paid to human perception of color and interactive 3D such as virtual reality.

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.