2024-01-08 - 2024-01-12 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 Deep 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 or online some weeks later. The project is voluntary, however, needed for those aiming for the ECTS points.
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 or online some weeks later. The project is voluntary, however, needed for those aiming for the ECTS points.
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 increase 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
Practical information (time, location ...)
Time : 2024-01-08 - 2024-01-12 (afternoons for work, skiing, wellness or whatever)
Location : Legendary Regina Hotel in Muerren, 2h from Bern with public transport: https://www.reginamuerren.ch/
Check-In: Your room is ready for occupancy from 3 pm on the day of arrival. Check-Out: On the day of departure, you are asked to vacate the room by 10 am and hand in the key at the reception
Fee students and UNIBE staff: 600 CHF (fee) + about 900 CHF (including room, breakfast, coffee break, lunch bag, dinner and social program).
Fee others: 1100 CHF (fee) + about 900 CHF (including room, breakfast, coffee break, lunch bag, dinner and social program).
Language: English
Participants : Max 20
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Fee others: 1100 CHF (fee) + about 900 CHF (including room, breakfast, coffee break, lunch bag, dinner and social program).
Language: English
Participants : Max 20
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Schedule
Hotel WiFi
REGMUR-Guest
+*Hotel@Guest23*+
REGMUR-Guest
+*Hotel@Guest23*+
Monday (Arrival)
14:00 - 14:45 Machine Learning Introduction (lecture, Sigve)
15:00 - 16:30 Tutorial (Sigve)
17:00 - 19:00 Apéro
19:00 - 20:00 Dinner (Regina)
Tuesday
08:00 - 08:45 Lecture 1 (Matthew)
09:00 - 10:00 Tutorial (Matthew)
10:00 - 10:30 Coffee break
10:45 - 12:30 Tutorial (Matthew)
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial (Matthew)
19:00 - 20:00 Dinner (Regina)
20:30 - 21:00 Neural Style Transfer (evening lecture, Sigve)
Wednesday
08:00 - 08:45 Lecture 2 (Matthew)
09:00 - 10:00 Invited talk
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Matthew)
17:00 - 18:30 Tutorial (Mykhailo)
19:00 - 20:00 Dinner (Regina)
20:00 - 22:00 Hang-Out in the Regina bar
14:00 - 14:45 Machine Learning Introduction (lecture, Sigve)
15:00 - 16:30 Tutorial (Sigve)
17:00 - 19:00 Apéro
19:00 - 20:00 Dinner (Regina)
Tuesday
08:00 - 08:45 Lecture 1 (Matthew)
09:00 - 10:00 Tutorial (Matthew)
10:00 - 10:30 Coffee break
10:45 - 12:30 Tutorial (Matthew)
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial (Matthew)
19:00 - 20:00 Dinner (Regina)
20:30 - 21:00 Neural Style Transfer (evening lecture, Sigve)
Wednesday
08:00 - 08:45 Lecture 2 (Matthew)
09:00 - 10:00 Invited talk
10:00 - 10:30 Coffee break
10:30 - 12:30 Tutorial (Matthew)
17:00 - 18:30 Tutorial (Mykhailo)
19:00 - 20:00 Dinner (Regina)
20:00 - 22:00 Hang-Out in the Regina bar
Thursday
08:15 - 08:45 Lecture 3 (Mykailo)
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, 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 "Bliemli Chäller"
Friday
08:15 - 08:45 Lecture 3 (Mykhailo)
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
08:15 - 08:45 Lecture 3 (Mykailo)
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, 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 "Bliemli Chäller"
Friday
08:15 - 08:45 Lecture 3 (Mykhailo)
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
Presentation Days:
Fill the poll.
Here you can announce your project idea and ask for collaborators or the other way around, join a project.
16.02.2024 09:30-12:00 None
19.02.2024 09:30-12:00 Keneni, Martin, Vladislav
For remote connection we use the zoom link below (login to see it), at the University we use room 227 in Sidlerstrasse 5.
Here you can announce your project idea and ask for collaborators or the other way around, join a project.
16.02.2024 09:30-12:00 None
19.02.2024 09:30-12:00 Keneni, Martin, Vladislav
For remote connection we use the zoom link below (login to see it), at the University we use room 227 in Sidlerstrasse 5.
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 info.dsl@unibe.ch.
You are free to bring familiy and friends of course (not participating in the school), if there are rooms free. Costs for additional travellers are on your own expenses.
Cancellation: Editions with less than 15 registrations will be cancelled one month in advance.
Arrival: Monday 08 of January 2024. School starts 14:00 and evening dinner is at 19:00.
Depature: Friday, 12 of January 2024 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 or Stechelberg.
You are free to bring familiy and friends of course (not participating in the school), if there are rooms free. Costs for additional travellers are on your own expenses.
Cancellation: Editions with less than 15 registrations will be cancelled one month in advance.
Arrival: Monday 08 of January 2024. School starts 14:00 and evening dinner is at 19:00.
Depature: Friday, 12 of January 2024 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 or Stechelberg.
Mürren can be reached from the Lauterbrunnen Valley via two connections:
- From Lauterbrunnen by cable car and a mountain railroad via Grütschalp to Mürren BLM.
- From Stechelberg by cable car via Gimmelwald to Mürren Schilthornbahnen LSMS.
Lauterbrunnen is easily accessible by train from Interlaken. The route via Stechelberg is mainly preferred by motorists because of the parking spaces at the Stechelberg valley station. Stechelberg can also be reached from Lauterbrunnen by post bus.
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
Dr. Mykhailo Vladymyrov (lectures and 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. Apparantly he is capable of skiing.
Dr. Matthew Vowels (lectures and tutorials)
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.
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.
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. Apparantly he is capable of skiing.
Dr. Matthew Vowels (lectures and tutorials)
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.
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.