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BEGIN:VEVENT
SUMMARY:Geordie Williamson (University of Sydney)
DTSTART:20220224T040000Z
DTEND:20220224T060000Z
DTSTAMP:20260422T212512Z
UID:MachineLearning/1
DESCRIPTION:by Geordie Williamson (University of Sydney) as part of SMRI S
 eminar Series: Machine learning for the working mathematician\n\nLecture h
 eld in Carslaw 273 & Online.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MachineLearning/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Adam Zsolt Wagner (ETH Zurich)
DTSTART:20220407T050000Z
DTEND:20220407T070000Z
DTSTAMP:20260422T212512Z
UID:MachineLearning/2
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MachineLearn
 ing/2/">A simple RL setup to find counterexamples to conjectures in mathem
 atics</a>\nby Adam Zsolt Wagner (ETH Zurich) as part of SMRI Seminar Serie
 s: Machine learning for the working mathematician\n\nLecture held in Carsl
 aw 273 & Online.\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MachineLearning/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Alex Davies
DTSTART:20220505T060000Z
DTEND:20220505T080000Z
DTSTAMP:20260422T212512Z
UID:MachineLearning/3
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MachineLearn
 ing/3/">A technical history of AlphaZero</a>\nby Alex Davies as part of SM
 RI Seminar Series: Machine learning for the working mathematician\n\nLectu
 re held in Carslaw 273 & Online.\n\nAbstract\nIn 2016 AlphaGo defeated the
  world champion go player Lee Sedol in a historic 5 game match. In this le
 cture we will discuss the research behind this system and the innovations 
 that ultimately lead to AlphaZero\, which can learn to play multiple board
  games\, including Go\, from scratch without human knowledge.\n
LOCATION:https://researchseminars.org/talk/MachineLearning/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Daniel Halpern-Leinster
DTSTART:20220511T230000Z
DTEND:20220512T010000Z
DTSTAMP:20260422T212512Z
UID:MachineLearning/4
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MachineLearn
 ing/4/">Learning selection strategies in Buchberger's algorithm</a>\nby Da
 niel Halpern-Leinster as part of SMRI Seminar Series: Machine learning for
  the working mathematician\n\nLecture held in Carslaw 273 & Online.\nAbstr
 act: TBA\n
LOCATION:https://researchseminars.org/talk/MachineLearning/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Lars Buesing
DTSTART:20220519T060000Z
DTEND:20220519T080000Z
DTSTAMP:20260422T212512Z
UID:MachineLearning/5
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MachineLearn
 ing/5/">Searching for Formulas and Algorithms: Symbolic Regression and Pro
 gram Induction</a>\nby Lars Buesing as part of SMRI Seminar Series: Machin
 e learning for the working mathematician\n\nLecture held in Carslaw 273 & 
 Online.\n\nAbstract\nIn spite of their enormous success as black box funct
 ion approximators in many fields such as computer vision\, natural languag
 e processing and automated decision making\, Deep Neural Networks often fa
 ll short of providing interpretable models of data. In applications where 
 aiding human understanding is the main goal\, describing regularities in d
 ata with compact formuli promises improved interpretability and better gen
 eralization. In this talk I will introduce the resulting problem of Symbol
 ic Regression and its generalization to Program Induction\, highlight some
  learning methods from the literature and discuss challenges and limitatio
 ns of searching for algorithmic descriptions of data.\n
LOCATION:https://researchseminars.org/talk/MachineLearning/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Qianxiao Li
DTSTART:20220526T050000Z
DTEND:20220526T070000Z
DTSTAMP:20260422T212512Z
UID:MachineLearning/6
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MachineLearn
 ing/6/">Deep learning for sequence modelling</a>\nby Qianxiao Li as part o
 f SMRI Seminar Series: Machine learning for the working mathematician\n\nL
 ecture held in Carslaw 273 & Online.\n\nAbstract\nIn this talk\, we introd
 uce some deep learning based approaches for modelling sequence to sequence
  relationships that are gaining popularity in many applied fields\, such a
 s time-series analysis\, natural language processing\, and data-driven sci
 ence and engineering. We will also discuss some interesting mathematical i
 ssues underlying these methodologies\, including approximation theory and 
 optimization dynamics.\n
LOCATION:https://researchseminars.org/talk/MachineLearning/6/
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