Gaussian Processes mini-course by Novi Quadrianto
From May, 31 to June, 7 Novi Quadrianto, an Assistant Professor of Sussex University (Great Britain) and an Academic Advisor of the International Laboratory of Deep Learning and Bayesian Methods, will read a mini-course "Gaussian processes".
Location: Faculty of Computer Science, Moscow, Kochnovsky Proezd, 3
Date and time: May 31 (room 509), June 2 (room 317), June 5 (room 509) and June 7 (room 205), 16 p. m. till 18 p. m.
Everybody is invited. If you are going to come, please register. If you need a pass, please specify it in the last question in the form.
Gaussian Process (GP) models are a versatile and useful tool for solving a variety of machine learning problems including regression, classification, and structured prediction. This mini-course will start from a basic introduction to how GPs offer a principled, practical, and probabilistic approach to non-linear modelling tasks. We will highlight how other frequently used methods are special cases of GPs, discuss more advanced topics such as approximate sparse methods, and conclude with some current research directions.
Prerequisites: A background in statistics, calculus, linear algebra, and computer science.
Novi Quadrianto is a famous young scientist whose research interests are Gaussian processes, Bayesian methods and machine learning. He has a lot of publications on the leading Computer Science conferences, he is an Associate Editor of IEEE Trans. on Pattern Analysis and Machine Intelligence. He worked with the most famous machine learning specialists (Alex Smola, Christoph H. Lampert, Zoubin Ghahramani).
The slides are available here (the password is the same as for HSE WiFi network)