IEEE Finland SPS/CAS Chapter Webinar: Three Components of Machine Learning

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Machine learning combines three components: data, model and loss function. We review how some widely used machine learning methods, such as linear regression or deep reinforcement learning, are obtained as combinations for specific choices for the three components. 


This webinar discusses three core components underyling machine learning methods: data, model and loss function. 

 



  Date and Time

  Location

  Hosts

  Registration



  • Date: 10 Nov 2020
  • Time: 11:00 AM to 12:57 PM
  • All times are Europe/Helsinki
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https://aalto.zoom.us/j/64325159192

  • Starts 25 October 2020 02:02 AM
  • Ends 10 November 2020 06:02 AM
  • All times are Europe/Helsinki
  • No Admission Charge


  Speakers

Alex Jung of Aalto University

Topic:

Machine Learning

Biography:

Alexander Jung received his Phd in signal processing from TU Vienna in 2012. After Post-Doc periods at TU Vienna and ETH Zurich he joined Aalto University as Assistant Professor for Machine Learning in 2015. Alex has first-authored more than ten paper in first-tier journals such as IEEE Transactions on Information Theory and IEEE Transactions on Signal Processing. He received a best student paper award at IEEE ICASSP 2011, an Amazon Web Services Machine Learning Award in 2018 and has been selected as Teacher of the Year at the Department of Computer Science in 2018. He currently serves as an associate editor for the IEEE Signal Processing Letters. 





  Media

Three Components of Machine Learning Slides for the Webinar 3.29 MiB