390043 UK VGSCO Statistical Inference via Convex Optimization (2018S)
Prüfungsimmanente Lehrveranstaltung
Labels
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 05.03.2018 12:00 bis Do 31.05.2018 23:59
- Abmeldung bis Do 31.05.2018 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine
Block, May 23 – June 4, 2018
Seminar Room 3.307 (3rd floor, Faculty of Business, Economics and Statistics)
Thursday, 24.05. 10:00 - 12:30
Friday, 25.05. 10:00 - 12:30Tuesday, 29.05. 15:00 - 17:30
Wednesday, 30.05. 10:00 - 12:30
Friday, 01.06. 10:00 - 12:30
Monday, 04.06. 10:00 - 12:30
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
To be graded, a participant should submit at the end of the classes solutions to two Exercises from Lecture Notes available at https://www2.isye.gatech.edu/~nemirovs/StatOpt_LN.pdf (selection of Exercises to be solved is up to participant). Preparing solutions is a take-home task with no restrictions on material used.
The grade will be based on the quality of the solution as assessed by the lecturer.
The grade will be based on the quality of the solution as assessed by the lecturer.
Mindestanforderungen und Beurteilungsmaßstab
Prüfungsstoff
Course contents as reflected in Exercises from Lecture Notes.
Literatur
a) Lecture Notes: Anatoli Juditsky, Arkadi Nemirovski "Statistical Inferences via Convex Optimization''
https://www2.isye.gatech.edu/~nemirovs/StatOpt_LN.pdfb) Transparencies
https://www2.isye.gatech.edu/~nemirovs/SCOTransp.pdf
https://www2.isye.gatech.edu/~nemirovs/StatOpt_LN.pdfb) Transparencies
https://www2.isye.gatech.edu/~nemirovs/SCOTransp.pdf
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 31.08.2018 08:43
What is in the scope of the course, are inference routines motivated and justified by Optimization Theory (Convex Analysis, Conic Programming, Saddle Points, Duality...), the working horse being convex optimization.
This choice is motivated by
- nice geometry of convex sets, functions, and optimization problems
- computational tractability of convex optimization implying computational efficiency of statistical inferences stemming from Convex Optimization.For more comments on "course's philosophy'' and for detailed description of course's contents, see Preface and Table of Contents in Lecture Notes available at
https://www2.isye.gatech.edu/~nemirovs/StatOpt_LN.pdf