Computer Science

This course focuses on the technical solutions needed to improve the fairness, accountability, and transparency of machine learning models. It reflects on the benefits and risks of machine learning models to develop methods to detect and mitigate biases and create solutions to make the inner workings of models more transparent. Topics include statistical notions of fairness and bias; the intended usage of machine learning models; learning fair representations; model interpretability and transparency; generating and evaluating model explanations; and probing representations for bias. Knowledge of machine learning (probability theory, linear algebra, classification) and programming is a prerequisite.

The course provides a theoretical understanding and practical skills related to object-oriented programming. Practical skills will be learnt using the C++ programming language. The course enables students to tackle complex programming problems, making good use of the object-oriented programming paradigm to simplify the design and implementation process.

This course offers the opportunity for students to implement cloud computing related ideas and complete the projects as groups. In this course, students also complete market and feasibility analyses. The goal of this course is to encourage students to create a product that can be successfully marketed.

This is a practical course that introduces the concepts of machine learning and application of algorithms to several types of available data samples.  Students are introduced to the Python programming language and key concepts related to the TensorFlow (TM) programming toolkit from Google. Programming skills are developed during this course to explore the potential benefits of deep learning algorithms. Students learn how to use scientific computing methods to handle, cleanse, transform, and validate data with the purpose of gaining insights from a wide range of datasets; how to present available data using charts, graphs, tables and more sophisticated visualization tools; how to model data and perform statistical analysis and ad hoc queries; and how to report on key findings and how to summarize and communicate results to mixed audiences. 

This is a comprehensive first course in computer communications and networks. The course introduces basic networking concepts, including protocol, network architecture, reference models, layering, service, interface, multiplexing, switching, and standards. An overview of digital communication from the perspective of computer networking is also provided. Topics include internet (TCP/IP) architecture and protocols, network applications, congestion/flow/error control, routing and internetworking, data link protocols, error detection and correction, channel allocation and multiple access protocols, communication media, and selected topics in wireless and data center networks. It covers recent advances in network control and management architectures by introducing the concepts of software-defined networking (SDN) and network (function) virtualization. Students gain hands-on experience in network programming using the socket API, network traffic/protocol analysis, and on assessment of alternative networked systems and architectures.

This course is intended for students in computing disciplines whose work focuses on human-computer interaction issues in the design of software systems. The course stresses the importance of user-centered design and usability in the development of software applications and systems. Students are taken through the analysis, design, development, and evaluation of human-computer interaction methods for software systems. They acquire hands-on design skills through laboratory exercises and assignments. The course also covers HCI design principles and emphasizes the importance of contextual, organizational, and social factors in system design.