Probabilistic models for data are ubiquitous in many areas of science and engineering, and specific domains such as visual and language understanding, finance, healthcare, biology, climate informatics, etc. This course will be an advanced introduction to probabilistic models of data (often through case studies from these domains) and a deep-dive into advanced inference and optimization methods used to learn such probabilistic models. This is an advanced course and ideally suited for student who are doing research in this area or are interested in doing research in this area.
Instructor’s consent. The course expects students to have a strong prior background in machine learning and probabilistic machine learning (ideally through formal coursework), probability and statistics, linear algebra, and optimization. The students must also be proficient in programming in MATLAB, Python, or R.
A tentative list of topics to be covered in this course includes
Treatment of the above topics will be via several case-studies/running-examples, which include generalized linear models, finite/infinite mixture models, finite/infinite latent factor models, matrix factorization of real/discrete/count data, sparse linear models, linear Gaussian models, linear dynamical systems and time-series models, topic models for text data, etc.
We will primarily use lecture notes/slides from this class. In addition, we will refer to monographs and research papers (from top Machine Learning conferences and journals) for some of the topics. Some recommended, although not required, books are:
Papers from conference/journals in machine learning and Bayesian statistics (e.g., ICML, NIPS, AISTATS, Journal of Machine Learning Research, Machine Learning Journal, Bayesian Analysis, Biometrika, Annals of Statistics, etc.)