Computer Science

Solving problems in areas such as business, biology, physics, chemistry, engineering, humanities, and social sciences often requires manipulating, analysing, and visualising data through computer programming. This course teaches students with little or no background in computer programming how to design and write small programs using a high-level procedural programming language, and to solve simple problems using these skills. On completion of this subject the student is expected to: 1.Use the fundamental programming constructs (sequence, alternation, selection) 2.Use the fundamental data structures (arrays, records, lists, associative arrays) 3.Use abstraction constructs such as functions 4.Understand and employ some basic program structures 5.Understand and employ some basic algorithmic problem solving techniques 6.Read, write, and debug simple, small programs

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.