2025-03-17 - 2025-03-21 Bern Winter School - Reinforcement Learning
Reinforcement Learning in Mürren
Reiter
About Bern Winter School on Reinforcement Learning
Bern Winter School on Reinforcement Learning (RL)
Learn 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 reinforcement 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 RL project (expected workload 30 hours) and present it in an in-person or online session at the University of Bern some weeks later. The project is voluntary, however, needed for those aiming for the ECTS points.
Learning outcomes, participants
Learn 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 reinforcement 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 RL project (expected workload 30 hours) and present it in an in-person or online session at the University of Bern some weeks later. The project is voluntary, however, needed for those aiming for the ECTS points.
Learning outcomes, participants
- Understand RL and different RL methods
- Implement RL systems using available libraries
- Evaluate and select appropriate RL methods for solving the task at hand
- Manage and create custom environments with the OpenAI Gym framework
- Train Q-Learning on one of the custom environments and perform hyper-parameter analysis
- Train Deep Reinforcement Learning methods on the CArt Pole and Bipedal Walker environments
- Apply RL to own tasks
- Implement RL systems using available libraries
- Evaluate and select appropriate RL methods for solving the task at hand
- Manage and create custom environments with the OpenAI Gym framework
- Train Q-Learning on one of the custom environments and perform hyper-parameter analysis
- Train Deep Reinforcement Learning methods on the CArt Pole and Bipedal Walker environments
- Apply RL to own tasks
- Understand the different aspects of RL and the different kinds of RL methods
- Apply RL to own tasks
- Apply RL to own tasks
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
Practical information (time, location ...)
Time : 2025-03-17 - 2025-03-21 (afternoons for work, skiing, wellness or whatever)
Location : Legendary Regina Hotel in Muerren, 2h from Bern with public transport: https://www.reginamuerren.ch/
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: 660 CHF (fee) + 900 CHF (accommodation costs including private room with shared bathroom, breakfast, coffee break, lunch bag, dinner, and social program). Additional consumption at the hotel needs to be paid directly upon check-out.
Fee others: 1160 CHF (fee) + 900 CHF (accommodation costs including private room with shared bathroom, breakfast, coffee break, lunch bag, dinner, and social program). Additional consumption at the hotel needs to be paid directly upon check-out.
Fee others: 1160 CHF (fee) + 900 CHF (accommodation costs including private room with shared bathroom, breakfast, coffee break, lunch bag, dinner, and social program). Additional consumption at the hotel needs to be paid directly upon check-out.
Language: English
Participants : Max 20
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Participants : Max 20
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Schedule
Monday (Arrival)
14:00 - 17:00 Machine Learning Refresher
17:00 - 19:00 Apero
19:00 - 20:00 Dinner (Regina)
Tuesday
08:15 - 08:45 Reinforcement Learning - Lecture 1
09:00 - 10:15 Tutorial
14:00 - 17:00 Machine Learning Refresher
17:00 - 19:00 Apero
19:00 - 20:00 Dinner (Regina)
Tuesday
08:15 - 08:45 Reinforcement Learning - Lecture 1
09:00 - 10:15 Tutorial
10:15 - 10:45 Coffee break
10:45 - 12:30 Tutorial
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial
19:00 - 20:30 Dinner (Regina)
20:30 - 21:00 Evening Tutorial
10:45 - 12:30 Tutorial
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial
19:00 - 20:30 Dinner (Regina)
20:30 - 21:00 Evening Tutorial
Wednesday
08:15 - 08:45 Reinforcement Learning - Lecture 2
09:00 - 10:15 Tutorial
10:15 - 10:45 Coffee break
10:45 - 12:30 Tutorial
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial
19:00 - 20:30 Dinner (Regina)
20:30 - 21:00 Evening Tutorial
08:15 - 08:45 Reinforcement Learning - Lecture 2
09:00 - 10:15 Tutorial
10:15 - 10:45 Coffee break
10:45 - 12:30 Tutorial
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:30 Tutorial
19:00 - 20:30 Dinner (Regina)
20:30 - 21:00 Evening Tutorial
Thursday
08:15 - 08:45 Reinforcement Learning - Lecture 3
09:00 - 10:15 Tutorial
10:15 - 10:45 Coffee break
10:45 - 12:30 Tutorial
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:00 Tutorial
18:45 - 21:30 Cheese Fondue at restaurant Allmendhubel (meet in front of Regina at 18:45)
21:30 - 22:00 Sledge ride down to the hotel
22:00 - XX:XX Disco Bliemli Chaeller
08:15 - 08:45 Reinforcement Learning - Lecture 3
09:00 - 10:15 Tutorial
10:15 - 10:45 Coffee break
10:45 - 12:30 Tutorial
12:30 - 17:00 Skiing, work or whatever
17:00 - 18:00 Tutorial
18:45 - 21:30 Cheese Fondue at restaurant Allmendhubel (meet in front of Regina at 18:45)
21:30 - 22:00 Sledge ride down to the hotel
22:00 - XX:XX Disco Bliemli Chaeller
Friday
08:15 - 08:45 Reinforcement Learning - Lecture 4
09:00 - 10:15 Tutorial
10:15 - 10:45 Coffee break and check out
10:45 - 11:45 Project brainstorming session
11:45 - 12:00 Wrap up
12:00 End of school
08:15 - 08:45 Reinforcement Learning - Lecture 4
09:00 - 10:15 Tutorial
10:15 - 10:45 Coffee break and check out
10:45 - 11:45 Project brainstorming session
11:45 - 12:00 Wrap up
12:00 End of school
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.
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.
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.
Cancellation: Editions with less than 10 registrations might be cancelled one month in advance. Cancellation is possible only until February 23th, 2025. No refunds will be made for cancellation received later or for no-shows. Moreover, participants will be charged by the University of Bern for the accommodation costs at Hotel Regina.The notice of cancellation needs to be submitted in written form 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.
Cancellation: Editions with less than 10 registrations might be cancelled one month in advance. Cancellation is possible only until February 23th, 2025. No refunds will be made for cancellation received later or for no-shows. Moreover, participants will be charged by the University of Bern for the accommodation costs at Hotel Regina.The notice of cancellation needs to be submitted in written form to info.dsl@unibe.ch
Arrival: Monday 17 of March 2025. School starts at 14:00 and evening dinner is at 19:00.
Depature: Friday, 21 of March 2025 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.
Depature: Friday, 21 of March 2025 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. If there is enough snow, there is a about 10 km cross country skiing slope in the Lauterbrünnen valley.
Leasure: Muerren offers spa, outstanding skiing slopes, swimming pool etc. If there is enough snow, there is a about 10 km cross country skiing slope in the Lauterbrünnen valley.
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
- 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
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. Lorenzo Brigato
Lorenzo Brigato is a Postdoctoral Researcher at the ARTORG center, a research institution affiliated with the University of Bern, and is currently involved in the application of AI to health and nutrition. He holds a Ph.D. degree in Computer Science from the Sapienza University of Rome, Italy. He earned an M.Sc. in Artificial Intelligence and Robotics with honors in 2018 from the same university. Previously, he obtained a B.Sc. in Engineering Sciences at the Tor Vergata University of Rome, Italy.
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. Lorenzo Brigato
Lorenzo Brigato is a Postdoctoral Researcher at the ARTORG center, a research institution affiliated with the University of Bern, and is currently involved in the application of AI to health and nutrition. He holds a Ph.D. degree in Computer Science from the Sapienza University of Rome, Italy. He earned an M.Sc. in Artificial Intelligence and Robotics with honors in 2018 from the same university. Previously, he obtained a B.Sc. in Engineering Sciences at the Tor Vergata University of Rome, Italy.
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