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DTSTAMP:20260707T174129Z
UID:13c6ba56-60cf-4f11-8ef7-7d56b8da32e7
DTSTART:20200217T083000Z
DTEND:20200219T153000Z
DESCRIPTION:The course offers basics of analyzing data with machine learnin
 g and data mining algorithms in order to understand foundations of learnin
 g from large quantities of data. This course is especially oriented toward
 s beginners that have no previous knowledge of machine learning techniques
 . The course consists of general methods for data analysis in order to und
 erstand clustering\, classification\, and regression. This includes a thor
 ough discussion of test datasets\, training datasets\, and validation data
 sets required to learn from data with a high accuracy. Easy application ex
 amples will foster the theoretical course elements that also will illustra
 te problems like overfitting followed by mechanisms such as validation and
  regularization that prevent such problems.\n\nThe tutorial will start fro
 m a very simple application example in order to teach foundations like the
  role of features in data\, linear separability\, or decision boundaries f
 or machine learning models. In particular this course will point to key ch
 allenges in analyzing large quantities of data sets (aka ‘big data’) i
 n order to motivate the use of parallel and scalable machine learning algo
 rithms that will be used in the course. The course targets specific challe
 nges in analyzing large quantities of datasets that cannot be analyzed wit
 h traditional serial methods provided by tools such as R\, SAS\, or Matlab
 . This includes several challenges as part of the machine learning algorit
 hms\, the distribution of data\, or the process of performing validation. 
 The course will introduce selected solutions to overcome these challenges 
 using parallel and scalable computing techniques based on the Message Pass
 ing Interface (MPI) and OpenMP that run on massively parallel High Perform
 ance Computing (HPC) platforms. The course ends with a more recent machine
  learning method known as deep learning that emerged as a promising disrup
 tive approach\, allowing knowledge discovery from large datasets in an unp
 recedented effectiveness and efficiency.\n\nPrerequisites: \nKnowledge on 
 job submissions to large HPC machines using batch scripts\, knowledge of m
 athematical basics in linear algebra helpful.\n\nParticipants should bring
  their own notebooks (with an ssh-client).\n\nLearning outcome:\nAfter thi
 s course participants will have a general understanding how to approach da
 ta analysis problems in a systematic way. In particular this course will p
 rovide insights into key benefits of parallelization such as during the n-
 fold cross-validation process where significant speed-ups can be obtained 
 compared to serial methods. Participants will also get a detailed understa
 nding why and how parallelization provides benefits to a scalable data ana
 lyzing process using machine learning methods for big data and a general u
 nderstanding for which problems deep learning algorithms are useful and ho
 w parallel and scalable computing is facilitating the learning process whe
 n facing big datasets. Participants will learn that deep learning can actu
 ally perform ‘feature learning’ that bears the potential to significan
 tly speed-up data analysis processes that previously required much feature
  engineering.\n\nCourse slides from the last training in February 2019 can
  be found at\n\n\nhttp://www.morrisriedel.de/prace-tutorial-parallel-and-s
 calable-machine-learning \n\nApplication \nApplicants will be notified one
  month before the course starts\, whether they are accepted for participit
 ation.\n\nInstructors: Prof. Dr. Morris Riedel\, Dr. Gabriele Cavallaro\, 
 Dr. Jenia Jitsev\, Jülich Supercomputing Centre\n\nContact \nFor any ques
 tions concerning the course please send an e-mail to g.cavallaro@fz-juelic
 h.de.\nhttps://events.prace-ri.eu/event/960/
SUMMARY:Parallel and Scalable Machine Learning @ JSC
URL;VALUE=URI:https://events.prace-ri.eu/event/960/
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