This course teaches the students practical skills needed for solving modern physics problems by means of computation. A number of examples illustrate the utility of numerical computations in various domains of physics.
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Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi
Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, w
Prof. Oleg Yazyev (Олег Язев) was born in Simferopol, Crimean peninsula. He obtained his degree in chemistry from Moscow State University in 2003. He then joined Ecole Polytechnique Fédérale de Lausanne (EPFL) completing his PhD thesis in chemistry and chemical engineering in 2007. Next two years he has spent as a postdoctoral fellow at the Institute of Theoretical Physics (ITP) and the Institute for Numerical Research in the Physics of Materials (IRRMA) of the same institution. In 2009-2011 he was a postdoctoral fellow at the Department of Physics of the University of California, Berkeley and the Lawrence Berkeley National Laboratory. In September 2011 he started an independent research group supported by the Swiss National Science Foundation professorship grant. In 2012 he was awarded an ERC Starting grant. His current research focuses on theoretical and computational physics of the recently discovered Dirac fermion materials with strong emphasis on their prospective technological applications. ResearcherID profile of Oleg Yazyev
Adaptive signal processing, A/D and D/A. This module provides the basic
tools for adaptive filtering and a solid mathematical framework for sampling and
quantization
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