2023-01-30 - 2023-02-03 Bern Winter School - Deep Learning
Machine Learning and Deep Neural Networks with TensorFlow tutorials in the ski resort Muerren.
Reiter
About Bern Winter School on Machine Learning
Bern Winter School on Deep Learning
Learn deep machine learning in the mornings and practise your own neural network (brain) while skiing or working in the afternoons.
About
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 grand 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 about two weeks later.
Learning outcomes, participants will
Learn deep machine learning in the mornings and practise your own neural network (brain) while skiing or working in the afternoons.
About
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 grand 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 about two weeks later.
Learning outcomes, participants will
- know what ML is
- know basic concepts of neural networks and learning
- know about design and usage of neutal nets
- can mangage basic operations in TensorFlow and know what a computational graph is
- can solve optimization problems
- can use neural networks in TensoFlow for supervised learning
- can visualize learning processes and computational graphs
- can apply TensorFlow for machine learning on own datasets in the project
Target group
- UNIBE staff, students and externals
Prerequisites
- 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
Methods
- 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)
Certificate
- 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.
Coaches
- The coaches are local and external experts
For CAS Applied Data Science Colleagues
- This winter school also counts as the Module 6 of the University of Bern Certificate of Advanced Studies (CAS) Applied Data Science.
Practical information (time, location ...)
Time : 2023-01-30 - 2022-02-03 (afternoons for work, skiing, wellness or whatever)
Location : Legendary Regina Hotel in Muerren, 2h from Bern with public transport: https://www.reginamuerren.ch/
Fee students and UNIBE staff: 600 CHF (fee) + 800 CHF (hotel etc) including full pension.
Fee others: 900 CHF (fee) + 800 CHF (hotel etc) including full pension.
Language: English
Participants : Max 20
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Location : Legendary Regina Hotel in Muerren, 2h from Bern with public transport: https://www.reginamuerren.ch/
Fee students and UNIBE staff: 600 CHF (fee) + 800 CHF (hotel etc) including full pension.
Fee others: 900 CHF (fee) + 800 CHF (hotel etc) including full pension.
Language: English
Participants : Max 20
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Schedule
Monday (Arrival)
14:00 - 14:45 Machine Learning Introduction (lecture, Sigve)
15:00 - 16:30 Tutorial (Sigve)
17:00 - 19:00 Apero
19:00 - 20:00 Dinner (Regina)
Tuesday
08:00 - 08:45 Lecture 1 (Matthew)
09:00 - 10:00 Tutorial (Mykhailo)
10:00 - 10:30 Coffee break
10:45 - 12:30 Tutorial (Mykhailo)
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial (Mykhailo)
19:00 - 20:00 Dinner (Regina)
20:00 - 20:30 Neural Style Transfer (evening lecture, Sigve)
Wednesday
08:00 - 08:45 Lecture 2 (Matthew)
09:00 - 10:00 Machine Ethics (Claus, remote)
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Mykhailo)
17:00 - 18:30 Tutorial (Mykhailo)
19:00 - 20:00 Dinner (Regina)
Thursday
08:00 - 08:45 Lecture 3 (Matthew)
09:00 - 10:00 Tutorial (Mykhailo)
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Mykhailo)
14:00 - 16:00 Curling Training at Muerren Sport Center
17:00 - 18:00 Project discussions (Mykhailo, Sigve)
18:45 - 21:00 Cheese Fondue at restaurant Allmendhubel (meet in front of Regina at 18:45)
21:00 - 21:30 Sledge ride down to the hotel
22:00 - 0X:X Blümlichäller
Friday
08:00 - 08:45 Lecture 3
09:00 - 10:15 Tutorial (Mykhailo)
10:00 - 10:40 Coffee break
10:40 - 11:45 Tutorial / discussion session (Mykhailo)
11:45 - 12:30 Wrap up (Sigve)
12:30 End of school
2023-03-20
13:30 Project presentations, Sidlerstrasse 5, 3012 Bern, office 227
14:00 - 14:45 Machine Learning Introduction (lecture, Sigve)
15:00 - 16:30 Tutorial (Sigve)
17:00 - 19:00 Apero
19:00 - 20:00 Dinner (Regina)
Tuesday
08:00 - 08:45 Lecture 1 (Matthew)
09:00 - 10:00 Tutorial (Mykhailo)
10:00 - 10:30 Coffee break
10:45 - 12:30 Tutorial (Mykhailo)
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial (Mykhailo)
19:00 - 20:00 Dinner (Regina)
20:00 - 20:30 Neural Style Transfer (evening lecture, Sigve)
Wednesday
08:00 - 08:45 Lecture 2 (Matthew)
09:00 - 10:00 Machine Ethics (Claus, remote)
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Mykhailo)
17:00 - 18:30 Tutorial (Mykhailo)
19:00 - 20:00 Dinner (Regina)
Thursday
08:00 - 08:45 Lecture 3 (Matthew)
09:00 - 10:00 Tutorial (Mykhailo)
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Mykhailo)
14:00 - 16:00 Curling Training at Muerren Sport Center
17:00 - 18:00 Project discussions (Mykhailo, Sigve)
18:45 - 21:00 Cheese Fondue at restaurant Allmendhubel (meet in front of Regina at 18:45)
21:00 - 21:30 Sledge ride down to the hotel
22:00 - 0X:X Blümlichäller
Friday
08:00 - 08:45 Lecture 3
09:00 - 10:15 Tutorial (Mykhailo)
10:00 - 10:40 Coffee break
10:40 - 11:45 Tutorial / discussion session (Mykhailo)
11:45 - 12:30 Wrap up (Sigve)
12:30 End of school
2023-03-20
13:30 Project presentations, Sidlerstrasse 5, 3012 Bern, office 227
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 (your notebook optionally with some 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 will be agreed upon during the course week.
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):
https://www.kaggle.com/datasets
https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
https://archive.ics.uci.edu/ml/index.php
https://www.openml.org/search?type=data
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 (your notebook optionally with some 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 will be agreed upon during the course week.
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):
https://www.kaggle.com/datasets
https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research
https://archive.ics.uci.edu/ml/index.php
https://www.openml.org/search?type=data
Registration, venue and travel
Registration: If you have an Ilias or AAI account (people affiliated with a Swiss higher education organisation), please login and join the course. For others, please write an email to scits@math.unibe.ch.
You are free to bring familiy and friends of course (not participating in the school), if there are rooms free.
Cancellation: The school may be cancelled by January 15 if there are not suffiecent participants. Cancellation of registrations before that are without any costs.
Arrival: Monday 30st of January 2023. School starts 14:00 and evening dinner is at 19:00.
Depature: Friday, 3th February 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 Lauterbruennen.
Leasure: Muerren offers spa, outstanding skiing slopes, swimming pool etc. Inform yourself: muerren.swiss/en/winter/
You are free to bring familiy and friends of course (not participating in the school), if there are rooms free.
Cancellation: The school may be cancelled by January 15 if there are not suffiecent participants. Cancellation of registrations before that are without any costs.
Arrival: Monday 30st of January 2023. School starts 14:00 and evening dinner is at 19:00.
Depature: Friday, 3th February 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 Lauterbruennen.
Leasure: Muerren offers spa, outstanding skiing slopes, swimming pool etc. Inform yourself: muerren.swiss/en/winter/
Readings
Ideally you prepare yourself with this python notebook before the school (download it and run it on colab)
- https://github.com/neworldemancer/DSF5/blob/master/Python_key_points_homework.ipynb
If you need some material for solving that notebook, you can use this book:
- https://github.com/jakevdp/PythonDataScienceHandbook
The lecture is largely based on this book: https://www.deeplearningbook.org/
- https://github.com/neworldemancer/DSF5/blob/master/Python_key_points_homework.ipynb
If you need some material for solving that notebook, you can use this book:
- https://github.com/jakevdp/PythonDataScienceHandbook
The lecture is largely based on this book: https://www.deeplearningbook.org/
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 coordinating the Data Science Lab at the University. Beyond science he likes philosophical conversations in the evening, Telemark skiing and friendly people.
Dr. Mykhailo Vladymyrov (tutorials)
Mykhailo is a trained physicist who worked at the Albert Einstein Institute of Fundamental physics (and beyond) with many years of experience with big data, machine learning and GPU computing. Today he is working for the Data Science Lab at the University. Mykhailo has a high level humor and view upon the human strive. You will enjoy his tutorials.
Dr. Matthew Vowels (lectures)
Matthew works for the Institute of Psychology (!) at the University of Lausanne. He knows what psychologists are doing with Machine Learning. He does ski, however, we haven't seen it.
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 coordinating the Data Science Lab at the University. Beyond science he likes philosophical conversations in the evening, Telemark skiing and friendly people.
Dr. Mykhailo Vladymyrov (tutorials)
Mykhailo is a trained physicist who worked at the Albert Einstein Institute of Fundamental physics (and beyond) with many years of experience with big data, machine learning and GPU computing. Today he is working for the Data Science Lab at the University. Mykhailo has a high level humor and view upon the human strive. You will enjoy his tutorials.
Dr. Matthew Vowels (lectures)
Matthew works for the Institute of Psychology (!) at the University of Lausanne. He knows what psychologists are doing with Machine Learning. He does ski, however, we haven't seen it.
