DATA8002 - Statistical Inference and Machine Learning for Network Data
Course Instructor
Professor Alec KIRKLEY
Assistant Professor
HKU Musketeers Foundation Institute of Data Science and
Department of Urban Planning and Design, HKU
Professor Alec Kirkley is a physicist interested in the theory of complex networks, statistical physics, as well as their applications to urban and social systems. The mathematical and computational methods he develops in his research draw on ideas from a range of disciplines including statistical physics, information theory, Bayesian inference, scientific computing, and machine learning. He received his PhD in Physics at the University of Michigan in 2021 under the supervision of Mark Newman and joined HKU as an Assistant Professor in 2022. His main research interests lie in developing principled unsupervised learning methods for noisy network data and improving the efficiency and interpretability of statistical inference methods for networks. He also adapts and applies these techniques to uncover new insights about the structure and dynamics of urban mobility as well as the underlying topology of geographical data.
Course Description
This course provides a concise and practical introduction to statistical inference and machine learning methods for analyzing network data, centered around three essential topics: community detection, network reconstruction, and graph representation learning. Within each of these topics, we will introduce the basic concepts then gradually move towards state-of-the-art techniques, along the way introducing key methods from statistical inference and machine learning that can be applied in more general contexts. We will illustrate the theory and algorithms with application examples from across the social and natural sciences. Students will gain hands-on experience through homework consisting of mathematical and/or programming exercises, as well as a final course project in the form of a research paper and presentation.
Prerequisites
A solid foundation in linear algebra, probability, and statistics will be important for understanding the course material. Such materials should at least be mastered at the level of MATH1853, but preferably at the levels of MATH2101/2102 for linear algebra and MATH3603 or STAT2601/2602 or STAT3902 for probability and statistics at HKU, or equivalent courses at other universities.
Some exposure to Bayesian inference, data structures and algorithms, machine learning, and optimization is also recommended but not strictly necessary.
If you’re curious about whether you would benefit from this course or your academic background is appropriate, contact the instructor for details.
HKU IDS
Research Postgraduate Programme
DATA8002 - Statistical Inference and Machine Learning for Network Data (Foundation)
Course Instructor
Prof Alec KIRKLEY
Course Description
This course provides a concise and practical introduction to statistical inference and machine learning methods for analyzing network data, centered around three essential topics: community detection, network reconstruction, and graph representation learning. Within each of these topics, we will introduce the basic concepts then gradually move towards state-of-the-art techniques, along the way introducing key methods from statistical inference and machine learning that can be applied in more general contexts. We will illustrate the theory and algorithms with application examples from across the social and natural sciences. Students will gain hands-on experience through homework consisting of mathematical and/or programming exercises, as well as a final course project in the form of a research paper and presentation.
Prerequisites
A solid foundation in linear algebra, probability, and statistics will be important for understanding the course material. Such materials should at least be mastered at the level of MATH1853, but preferably at the levels of MATH2101/2102 for linear algebra and MATH3603 or STAT2601/2602 or STAT3902 for probability and statistics at HKU, or equivalent courses at other universities.
Some exposure to Bayesian inference, data structures and algorithms, machine learning, and optimization is also recommended but not strictly necessary.
If you’re curious about whether you would benefit from this course or your academic background is appropriate, contact the instructor for details.