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2020-09-01 CAS AML M2 Deep Networks

CAS Advanced Machine Learning Module 2

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About
CAS Advanced Machine Learning Module 2
 
See study plan on math.unibe.ch/cas_aml 

Learning outcomes
  • know established and commonly used approaches to deep learning
  • can design, train, tune and regulate deep feedforward, convolutional and recurrent neural networks
Learning objectives
  • Deep feedforward networks
  • Regularisation for deep learning
  • Training and optimisation for deep models
  • Convolutional networks
  • Sequence modelling and recurrent neural networks
Target group
  • Graduates and professionals enrolled for the CAS Applied Data Science 
Prerequisites 
  • University or University of Applied Sciences level degree (bachelor, master, phd) 
Methods
  • Lectures, tutorials, discussions, project work with presentation.
Practical information (time, location ...)
Time : 2020-09-01 - 04 09:00 - 12:30
Location : Fabrikstrasse 8 , room B005, University of Bern

Language: English
Participants : Max 24
Registration : Mandatory (via Ilias or email to responsible)
Responsible : PD Dr. Sigve Haug
Schedule
Module 1 Deep Networks
 
Tuesday 2020-09-01
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30 - 13:30 Lunch
13:30 - 17:00 Individual work with notebooks

Wednesday 2020-09-02
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30 - 13:30 Lunch
13:30 - 17:00 Individual work with notebooks
 
Thursday 2020-09-03
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30 - 13:30 Lunch
13:30 - 17:00 Individual work with notebooks

Friday 2020-09-04
09:00 - 09:30 Introduction
09:30 - 10:30 TBD
11:00 - 12:30 TBD
12:30 - 13:30 Lunch
13:30 - 17:00 Individual work with notebooks
Datasets
During the CAS you ideally work on your own datasets from work, research or private project. However, the lecturers can also you provide you with links to suitable datasets. 
Readings
The CAS content follows more or less the chapters 5 to 20 in the https://www.deeplearningbook.org.
Lecturers and Coaches
Dr. Geraldine Conti (lecturer)

Geraldine has a PhD from EPFL, has been a research associate at Harvard, a CERN fellow, a research associate at Disney Research, associated professor in machine learning and is now head of the machine learning group at PAG.

PD Dr. Sigve Haug (director of studies)

Sigve studied physics in Germany, Spain and Norway. He has been involved in neutrino physics experiments and high energy frontier experiments, often with main focus on the computing challenges related to the large and distributed data from these experiments. Today he is working for the Albert Einstein Center for fundamental Physics and the Mathematical Institute of the University of Bern where is leading the Science IT Support unit.