Advanced Social Computing (COMP5800)

This course is an introduction to Machine Learning (ML) and Natural Language Processing (NLP) for Social Computing. It introduces the background and current states of social networks and their analysis in terms of content, users, social relations and applications, and covers the fundamentals of graph and text processing. The course has a particular emphasis on key advancements in the area of representation learning on graph and text data - with bias toward the latter, reflecting instructor biases. Students are expected to research innovative ideas in this context and practically investigate them on real world datasets. At the end of this course, students should have good understanding of the background, design, analysis and implementation of social media analysis systems, as well as hands-on experience on a range of tasks from identifying important nodes to detecting communities to tracing information diffusion in social networks. Special emphasis will be given to understanding novel ML and NLP techniques and using them in practice. Guest lectures by distinguished researchers and course projects emphasize subtleties of translating ML and NLP into practical applications in social networks. In order to succeed in this course, students should have a strong interest in conducting (or learning how to conduct) research. Prior exposure to ML or (statistical) NLP is recommended but not strictly required. Familiarity with linear algebra, (basic) calculus, and probability will be assumed throughout the course.