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2022-09-19 ~ 2022-12-06
Lecturer Fan Yang (Research Fellow)
Time 09:50 - 11:25, Mon,Tue
Online Zoom: 537 192 5549 PW:BIMSA
Understand discrete and continuous random variables, transformations and expectations, common families of distributions, multiple random variables, differential and integral calculus.
This course covers theoretical and applied fundamentals of statistical inference. The primary topics include principles of data reduction, point estimation, hypothesis testing, interval estimation and asymptotic methods.
Dr. Fan Yang is a Professor at Yau Mathematical Sciences Center. She obtained her Ph.D. in Applied Mathematics and Computational Science from the University of Pennsylvania in 2014. Dr. Yang’s research interests center around the development of statistical methodologies for causal inference problems inspired by scientific applications, ranging from public health to genomics and educational research. As a first or corresponding author, she has published multiple papers in leading journals in statistics and genetics, including Annals of Applied Statistics, Bioinformatics, Biometrics, Genetic Epidemiology, Genome Biology, Genome Research, and Journal of the Royal Statistical Society Series B. During her PhD study, she received the American Statistical Association (ASA) Section on Statistics in Epidemiology Young Investigators Award in 2013 and ASA Section on Health Policy Statistics Student Paper Competition Award in 2014. She is currently serving as an Associate Editor for Biometrics, one of the leading methodological journals in the biostatistics profession.
2022-10-27 ~ 2023-01-12
Bayesian machine learning
Lecturer Alexey Zaytsev (Visiting Assistant Research Fellow)
Time 17:05 - 18:40, Thu
Online Zoom: 928 682 9093 PW:BIMSA
Probability theory, Mathematical statistics, Machine learning
Probabilistic approach in machine and deep learning leads to principled solutions. It provides explainable decisions and new ways for improving of existing approaches. Bayesian machine learning consists of probabilistic approaches that rely on Bayes formula. It can help in numerous applications and has beautiful mathematical concepts behind. In this course, I will describe the foundations of Bayesian machine learning and how it works as a part of deep learning framework.
Alexey has deep expertise in machine learning and processing of sequential data. He publishes at top venues, including KDD, ACM Multimedia and AISTATS. Industrial applications of his results are now in service at companies Airbus, Porsche and Saudi Aramco among others.