Convex Optimization for Machine Learning - Suh, Changho (KAIST, South Korea)
168,99 €
inkl. MwSt.
Versandkostenfrei*
Versandfertig in 1-2 Wochen
  • Gebundenes Buch

Provides an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts, with a particular emphasis on machine learning.

Andere Kunden interessierten sich auch für
Produktbeschreibung
Provides an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal is to help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts, with a particular emphasis on machine learning.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Autorenporträt
Dr. Changho Suh is an Associate Professor of Electrical Engineering at KAIST. He received the B.S. and M.S. degrees in Electrical Engineering from KAIST in 2000 and 2002 respectively, and the Ph.D. degree in Electrical Engineering and Computer Sciences from UC Berkeley in 2011. From 2011 to 2012, he was a postdoctoral associate at the Research Laboratory of Electronics in MIT. From 2002 to 2006, he was with Samsung Electronics.Prof. Suh is a recipient of numerous awards in research and teaching: the 2022 Google Research Award, the 2021 James L. Massey Research & Teaching Award for Young Scholars from the IEEE Information Theory Society, the 2020 LINKGENESIS Best Teacher Award (the campus-wide Grand Prize in Teaching), the 2019 AFOSR Grant, the 2019 Google Education Grant, the 2018 IEIE/IEEE Joint Award, the 2015 IEIE Haedong Young Engineer Award, the 2015 Bell Labs Prize finalist, the 2013 IEEE Communications Society Stephen O. Rice Prize, the 2011 David J. Sakrison Memorial Prize (the best dissertation award in UC Berkeley EECS), the 2009 IEEE ISIT Best Student Paper Award, and the five Department Teaching Awards (2013, 2019, 2020, 2021, 2022). Dr. Suh is an IEEE Fellow, a Distinguished Lecturer of the IEEE Information Theory Society from 2020 to 2022, the General Chair of the Inaugural IEEE East Asian School of Information Theory 2021, an Associate Head of the KAIST AI Institute from 2021 to 2022, and a Member of the Young Korean Academy of Science and Technology. He is also an Associate Editor of Machine Learning for IEEE TRANSACTIONS ON INFORMATION THEORY, a Guest Editor for the IEEE JOURNAL ON SELECTED AREAS IN INFORMATION THEORY, the Editor for IEEE INFORMATION THEORY NEWSLETTER, an Area Editor for IEEE BITS the Information Theory Magazine, an Area Chair of NeurIPS 2021-2022 and a Senior Program Committee of IJCAI 2019-2021.