Miss Wenrui Zuo
Email: email@example.comRoom Number: Engineering, Eng 104
Data Mining (Postgraduate/Undergraduate)
Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.
Digital Media and Social Networks (Postgraduate)
Content description: ------- How does the way we feel and express emotional behaviour affect our interaction with technology? What if we could use a ''head nod'' for ''liking'' things on Facebook? Can we create assistive technology to help people suffering from social disorders (e.g., autism)? Affective and Behavioural Computing is a multidisciplinary field of research and practice concerned with these questions, and understanding, recognizing and utilizing human emotions and communicative behaviour in the design of computational systems. ----- The following list aims to clarify the content and provides a representative list of topics: ¿ Overview: affective and behavioural computing; ¿ Theories in psychology, cognitive science and neuroscience: affect, emotion and social signal processing; ¿ Computational models; ¿ Emotion, affect and social signals in Human-Computer Interaction (HCI); ¿ Sensing: vision, audio, bio signals, text; data acquisition and annotation, databases and tools; ¿ Processing: extracting meaningful information and features; ¿ Recognition: applying machine learning techniques; ¿ Programming refresher: Hands-on lecture on programming for affective and behavioural computing using relevant libraries; ¿ Evaluation: automatic analysers, and emotionally and socially intelligent systems; ¿ Affect and social signal expression and generation (virtual characters, robots, etc.); ¿ Affect and social signals for Mobile HCI; ¿ Applications (entertainment technology/gaming/arts; clinical and biomedical studies, e.g., autism, depression, pain; etc.; implicit (multimedia) tagging; affective wearables); ¿ Ethical issues.
Digital Media and Social Networks (Undergraduate)
Introduction to Online Social Networks (OSN) Characteristics of OSNs Basic Graph Theory Small World Phenomenon Information propagation on OSNs Influence and Content Recommendation Sentiment Analysis in Social Media Privacy and ethics