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
Probability and Matrices (Undergraduate)
This module covers: Probability theory Counting permutations and combinations Conditional probabilities Bayesian probability Random variables and probability models Vector and matrix algebra Linear equations Vector spaces Linear combinations, linear independence