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2020-01-27 - 2020-01-31 Bern Winter School on Machine Learning 2020 (ONLY FOR CAS ADS)

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


About Bern Winter School on Machine Learning
Bern Winter School on Machine Learning (ML) - ONLY FOR CAS ADS PARTICIPANTS
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
  • have an overview of ML, AI and applications areas
  • know basic concepts of neural networks and learning
  • know about design and  usage of machine learning algorithms  
  • 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
  • UNIBE staff, students and externals
  • You must bring your own laptop
  • Mathematics and statistics at the level of an introductionary course on university level 
  • Basic Python knowledge (if you don't have, take one of our Python training courses:
  • 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 (can be skipped if you don't want the ECTS points, but own work and presentation increases your skills dramatically)
  • A certificate will be delivered to participants who have attended the whole training and presented their project work successfully. The school yields 2 ECTS points. 
  • The coaches are local and external experts
For CAS Applied Data Science Colleagues
  • This winter school is also the Module 3 of the University of Bern Certificate of Applied Studies (CAS) Applied Data Science. 
Practical information (time, location ...)
Time : 2020-01-27 - 2020-01-31 (afternoons for work, skiing, wellness or whatever) 
Location : Legendary Regina Hotel in Muerren, 2h from Bern with public transport
Fee: 600 CHF (free of charge for CAS Applied Data Science participants), lodging in addition.

Language: English
Participants : Max 24
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Monday 2020-01-27 (Arrival)
19:00 - 20:00 Dinner in Hotel Regina
Tuesday 2020-01-28
08:00 - 08:45 Machine Learning Review (lecture, Geraldine)
09:00 - 09:45 Tutorial I (Mykhailo)
09:45 - 10:15 Coffee break
10:15 - 11:30 Tutorial I (Mykhailo)
11:30 - 12:15 Deep Feedforward Networks - Introduction (lecture, Geraldine)
12:15 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial II (Mykhailo), ML4Muerren Competition Instructions
              (Register your project idea here:
19:00 - 21:00 Dinner at Eiger Guesthouse
21:00 - 03:30 Fun

Wednesday 2020-01-29
08:00 - 08:45 Deep Feedforward Networks - Regularization, Optimisation (lecture, Geraldine)
09:00 - 09:45 Invited talk TBC (or tutorial, Mykhailo)
09:45 - 10:15 Coffee break
10:15 - 12:15 Tutorial III (Mykhailo)
13:00 - 15:00 (Module 2 presentaitions, first slot reserved)
12:15 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial IV (Mykhailo) (or lecture 4?)
19:00 - 20:00 Dinner
20:00 - 21:30 The AI/ML for Muerren competition (

Thursday 2020-01-30
08:00 - 08:45 Trends in Machine Learning (Radhakrishna Achanta)
09:00 - 09:45 Artificial Intelligence and Ethics (Claus)
09:45 - 10:15 Coffee break
10:15 - 12:15 Tutorial V (Mykhailo)
17:00 - 18:30 Project discussions (Mykhailo, Sigve, Geraldine)
19:00 - 20:00 Dinner
20:00 - 20:30 Machine Learning Use Cases (Radhakrishna Achanta)

Friday 2019-02-31
08:00 - 08:45 Computational optimal transport (Kinga, lecture)
09:00 - 09:45 Tutorial VI
09:45 - 10:15 Coffee break
10:15 - 11:15 Tutorial VII (Mykhailo) 
11:15 - 12:00 Wrap up (Sigve) 
12:00         End of school

Slots for presentations (please register here for one slot :

Monday 2020-03-16 14:00-16:30 or 17:00-19:30 
Friday 2020-03-20 14:00-16:30 or 17:00-19:30 
New Location:
Project Instructions
For the 2 ECTS certificate you need to do a project:

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 (please register here for one slot :

Monday 2020-03-16 14:00-16:30 or 17:00-19:30 
Friday 2020-03-20 14:00-16:30 or 17:00-19:30 
Location: University of Bern, Exakte Wissenschaften, Sidlerstrasse 5, room 228 

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, venue and travel
Registration: Here on Ilias

Additionally you have to book a room at the Regina Hotel in Muerren (included in the CAS fee). You are free to bring familiy and friends of course (not participating in the school), if there are rooms free.
Arrival:            Monday, 27. of January 2020 (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, 31. of January 2020 at noon (if you don't stay longer for your pleasure)
Book your stay (single or double room, bathrooms on the floor) at legendary hotel Regina by email (you must give booking code „BWSML“):,

Travel:             By public transport 2 hours from Bern ( Muerren is a car free village. You can park in Lauterbruennen.                

Leasure:          Muerren offers spa, outstanding skiing slopes, swimming pool etc. Inform yourself:
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:
  • Pattern Recognition and Machine Learning by Christopher M. Bishop
  • Machine Learning: a Probabilistic Perspective  by Kevin P. Murphy
  • Varoius courses on etc : Machine Learning @ Stanford (Andrew Ng)
Listenings for skiing
  • 1  2 3 4 5 6 7
Lecturers and Coaches
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. Geraldine Conti (theory)

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.
Her skiing skills are still unknown.

Dr. Mykhailo Vladymyrov (tutorials)

Mykhailo is the best tutor we ever had. He is a trained physicist working at the Albert Einstein Institute of Fundamental physics (and beyond) with many years of experience with big data, machine learning and GPU computing. Mykhailo has a high level humor and view upon the human strive. You will enjoy his tutorials.

Prof. Dr. Dr. Claus Beisbart

Claus is our school philosopher and helps with the bigger picture and ethical aspects.

More to come