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VERSION:2.0
PRODID:-//Memento EPFL//
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SUMMARY:Deep Learning to assist in Human Learning
DTSTART:20170718T101500
DTEND:20170718T111500
DTSTAMP:20260503T111326Z
UID:a487a67fc6b0f6e2cd44fbc93b550556de109bd0531e2a337fe59ff2
CATEGORIES:Conferences - Seminars
DESCRIPTION:Chris Piech\, Stanford University \nWhen we observe thousands 
 and sometimes millions of students learning simultaneously what patterns u
 nfold? \n\nBeing able to autonomously understand students as they learn\,
  both in terms of assessing knowledge and being able to provide feedback\,
  is a grand challenge in education and one that has been studied for hundr
 eds of years. The simultaneous advent of massive education datasets\, and 
 machine learning methods which are able to understand complex data provide
 s a unique opportunity to readress these hard problems in education.\n\nWh
 en we apply artificial neural network methods to data from thousands and s
 ometimes millions of students learning\, clear and useful patterns unfold.
  (1) Using recurrent neural networks we are able to predict how students w
 ill respond to future questions\, and (2) through the use of tree neural n
 etworks we can understand commonalities in student code as they learn to p
 rogram.\n\nWhile deep learning has served as a breakthrough for massive on
 line education\, most learning occurs in small contexts. The final part of
  this talk will speak to the open machine learning problem of how we can p
 rovide artificial intelligence for educational settings with few or even n
 o historical data.\n\nDr. Chris Piech is a Lecturer of Computer Science at
  Stanford University where he teaches both introduction to programming and
  introduction to artificial intelligence and runs a research lab. He is a 
 recipient of the George E. Forsyth Memorial Teaching Award.\n \nIn his re
 search Chris has developed algorithms\, especially deep learning architect
 ures\, to better understand how humans learn both generally and in the con
 text of programming education. He is especially known for pioneering Deep 
 Knowledge Tracing and Tuned Peer Grading. The algorithms that he has devel
 oped are run at Stanford\, on Code.org\, on Khan Academy and on Coursera i
 n order to teach and provide feedback to millions of students.  Chris is 
 currently working on methods for "zero shot artificial intelligence tutors
 \," with the goal of developing autonomous tutors that can effectively und
 erstand and help students who are working through assignments when there i
 s no prior student data. \n \nChris was born in Nairobi\, Kenya and spen
 t his childhood between Nairobi and Kuala Lumpur\, Malaysia.\n 
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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