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2023-01-23 - 2023-01-27 CAS ADS M6 Deep Learning (ONLY FOR CAS ADS)

Machine Learning and Deep Neural Networks with TensorFlow tutorials in the ski resort Muerren.


Learn machine learning in the mornings and practise your own neural network (brain) while skiing or working in the afternoons.

Due to increased access to data and compute capacity Machine Learning and Artificial Intelligence have become useful in many areas. In research and industry it is applied in various fields. Image recognition, online discrimination, natural language treatment, robotics, omics ... you name it.

In this winter school on machine learning you attend lectures and tutorial sessions over four mornings. This happens in Muerren, a great ski resort, in the cool old hotel Regina. You make your own ML project (expected workload 30 hours) and present it in an in-person session at the University of Bern two weeks later.

Learning outcomes, participants will
  • know basic concepts of neural networks and learning
  • can mangage basic operations in TensorFlow and know what a computational graph is
  • can solve optimization problems
  • can use neural networks in TensoFlow for digit recognition
  • can visualize learning processes and computational graphs in TensorBoard
  • can process images and signals with deep convolutional networks
  • can apply TensorFlow for machine learning on own datasets
Target group
  • CAS Applied Data Science Students
  • You must bring your own laptop
  • Mathematics and statistics at the level of an introductionary course on university level
  • Basic Python knowledge
  • The training is as language independent as possible, but examples and practical work is in Python
  • Theoretical lectures, evening talks, tutorials (with Jupyter notebooks), project work with presentation or report
  • The coaches are local and external experts
Time : 2023-01-23 - 2023-01-27 (afternoons for work, skiing, wellness or whatever) 
Location : Legendary Regina Hotel in Muerren, 2h from Bern with public transport. Remote participation will be possible.
Fee: free of charge for CAS Applied Data Science participants.

Language: English
Participants : Max 24
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Monday (Arrival)
17:00 - 19:00 Hang out in the lobby
19:00 - 20:00 Dinner at Hotel Regina
08:00 - 09:00 Deep Feedforward Networks - Introduction (Matthew)
09:15 - 10:15  Tutorial (Mykhailo)
10:15 - 10:45 Coffee break
10:45 - 12:30 Tutorial (Mykhailo)
12:15 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial (Mykhailo)
19:00 - 20:00 Dinner at hotel Regina
20:00 - 20:45 Neural Style Transformations (an example on how to use deep ML to create art)

08:00 - 08:45 Deep Feedforward Networks - Regularization, Optimisation (Matthew)
09:00 - 10:00 Machine Ethics (Claus)
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Mykhailo)
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial (Mykhailo) 
19:00 - 20:00 Dinner at hotel Regina

08:00 - 08:45 Lecture (Matthew)
09:00 - 10:00 Tutorial (Mykhailo)
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Mykhailo) 
17:00 - 18:00 Project discussions (Mykhailo, Matthew)
19:00 - 22:00 Fondue and sledge ride back down to Regina in the dark
                       Everyone has to be READY OUTSIDE THE HOTEL at 18:45.
                       The train up departs at 19:00.

08:00 - 08:45 Lecture (Matthew)
09:00 - 10:00 Tutorial (Mykhailo)
10:00 - 10:30 Coffee break and Check Out
10:30 - 11:15 Tutorial / Discussion Session (Mykhailo)
11:15 - 12:00 Wrap up (Mykhailo, Matthew) 
12:00             End of school
Presentations dates:

March 6. 13:30-17:00    (location: ExWi, Sidlerstr 5, room 228)
13:30 Lisa, Emilie, Kim
14:00 Romain
14:30 Jürg and Nils
15:00 Jonas and Marco
March 13. 13:30-17:00 (location: UniS Schanzeneckstrasse 1, A-124)
13:30 Claudia, Nathalie and Jani
14:00 Marc and Stefano
14:30 Majdah  |  Davi
15:00 Raphael & Michael
15:30 Barbara, Naima, Zita
16:00 Laura and Filipe
16:30 Frederic and Nicolas
17:00 Michael, Nico and Leonard
Goal: Apply what has been learned in the tutorials to a similar or different task (T) on own or public data (E) and ideally assess the performance (P) of the task solving.

Expected effort: 30 hours

Result: 15 minutes presentation (max 10 slides) to be uploaded to Ilias together with the Jupyter notebook or Python script used (Naming convention: surname_1-surname_2-projectname.pdf/ipynb)

Teamwork:Please work and present in teams of two (or three). Exceptionally you can work alone.

Slots for presentations - TBD

Assessment: You will get feedback (15 minutes) right after your presentation. If you have given it a good try (~30h) your project will pass. There is no further grading. The project together with school attendance yield 2 ECTS credit points.

Links with public datasets you may use  (you better choose something easy, i.e. well formatted):
Registration: Here on Ilias
You are free to bring familiy and friends of course (not participating in the school). Any additional arrangements with the hotel regarding this, you have to organize yourself.  
Arrival:            Monday, 23. of January 2023 (evening dinner at 19:00). School starts Tuesday morning at 08:00. (With the 06:04 train on Tuesday you can make the Tuesday morning if you don't want to arrive on Monday)
Depature:       Friday, 27. of January 2023 at noon (if you don't stay longer for your pleasure)
Travel:             By public transport 2 hours from Bern (sbb.ch). Muerren is a car free village. You can park in Stechelberg.      

Leasure:          Muerren offers spa, outstanding skiing slopes, swimming pool etc. Inform yourself:  muerren.swiss/en/winter/
  • You will profit a lot more from the school if you read a bit up front in these online resources (google it)The lecture is largely based on this book: https://www.deeplearningbook.org/
Further resources:
Pattern Recognition and Machine Learning by Christopher M. Bishop.
Machine Learning: a Probabilistic Perspective by Kevin P. Murphy.
Varoius courses on Coursera.org etc : Machine Learning @ Stanford (Andrew Ng)
Dr. Matthew Vowels

His skiing skills are unknown to us. We have been told that his DL skills are excellent. 

PD Dr. Sigve Haug (overview, school responsible)

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. Beyond science he likes philosophical conversations in the evening, Telemark skiing and friendly people. 

Dr. Mykhailo Vladymyrov (tutorials)

Mykhailo is a trained physicist from the Albert Einstein Institute of Fundamental physics with many years of experience with big data, machine learning and GPU computing. He is now working for the Theodor Kocher Institute and the Mathematical Institute. Mykhailo has a high level humor and view upon the human strive. You will enjoy his tutorials.

Lecturers from previous years:
Dr. Rahdakrishna Achanta, Swiss Data Science Center
Simon Jenni, University of Bern
Prof. Dr. Paolo Favaro, University of Bern
Dr. Fernando Perez-Cruz, Swiss Data Science Center
Dr. Qiyang Hu, Computer Science, University of Bern
Dr. Geraldine Schaller Conti 
Prof. Dr. Dr. Claus Beisbart