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Symbol Kurs

2022-10-03 - 2022-10-07 CAS AML Module 3 - Deep Learning Research

Deep Neural Networks - Advanced Topics (M3 CAS AML)


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. With this school we target people already familiar with machine learning who would like learn about this year's topics (see below).  

In this school you attend lectures and tutorial sessions over four mornings and three afternoons. This happens in a great hotel on Mallorca, a great resort for hiking and water sport, far away from La Palma and Ballermann. Please fill out the doodle sent out to you to inform us of your in-person participation on Mallorca at the hotel Es Blau des Nord: https://www.esblaudesnord.com/en/. All lectures are held in hybrid mode and you can access on-the-spot or online. 

Topics 2022
- Autoencoders
- Reinforcement Learning

Target group

- Enrolled people for CAS Advanced Machine Learning
- Other interested people with the required prerequisites

- You must bring your own laptop- Mathematics and statistics at the level of an introductionary course on university level- Python knowledge ABSOLUTELY required
- Data analysis and Machine Learning experiences The training is as language independent as possible, but examples and practical work is in Python

Lectures based on Jupyter notebooks, evening talks, tutorials (with Jupyter notebooks), project work with presentation.

A certificate will be delivered to participants who have attended the whole training and presented their project work successfully. The module corresponds to 2 ECTS points.


The coaches are local and external experts
Time : 2022-10-03 - 2022-10-07 (afternoons for work, swimming, hiking, wellness or whatever) 
Location Mallorca: https://www.esblaudesnord.com/en/
URL: Login to Ilias to see the link. 

Course Fee : For CAS AML participants there is no fee and full pension hotel (three meals) is included in the CAS fee, For others the fee is 600 CHF or 900 CHF with project and 2 ECTS points. Others also have to pay the hotel. All participants arrange and pay their travels themselves.     

Language: English
Participants : Max 24
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Monday 2022-10-03 (Arrival)
17:00 - 19:00 Apero in the patio
19:30 - 21:30 Dinner

Tuesday 2022-10-04
08:30 - 08:45 Welcome and Module Introduction (Sigve) 
08:45 - 10:30 Autoencoders (Mykhailo)
10:30 - 11:00 Break
11:00 - 12:30 Autoencoders (Mykhailo)
12:30 - 17:00 Beach, sport, hiking or whatever
17:00 - 19:00 Autoencoders (Mykhailo)
19:30 - 21:30 Dinner at hotel

Wednesday 2022-10-05

08:30 - 10:30 Autoencoders (Mykhailo)
10:30 - 11:00 Coffee break
11:00 - 12:30 Autoencoders (Mykhailo)
12:30 - 17:00 Leisure
17:00 - 19:00 Autoencoders (Mykhailo)
19:30 - 21:30 Dinner at the hotel

Thursday 2022-10-06
08:30 - 10:30 Reinforcement Learning (Lorenzo)
10:30 - 11:00 Break
11:00 - 12:30 Reinforcement Learning (Lorenzo)
12:30 - 17:00 Beach, sport, hiking, work or whatever
17:00 - 19:00 Reinforcement Learning (Lorenzo)
19:30 - 21:30 Dinner at hotel

Friday 2022-10-07 
08:30 - 10:30 Reinforcement Learning (Lorenzo)
10:30 - 11:00 Coffee break and check out
11:00 - 11:45 Reinforcement Learning (Lorenzo)
11:45 - 12:30 Wrap up
12:30             End of module

Presentation Days 
Please choose your slot here. Login to find URL for remote presentation. Physical location is TBD.

2022-12-13 09:00-12:30 Reinforcement Learning Projects, Sidlerstrasse 5, Room 228, login to Ilias for Zoom link
2022-12-19 13:30-17:00 Autoencoder Projects, Hochschulstrasse 4, Room 104, Main Building
To pass Module 3 you need to peform a small (30h) project and present.

Expected effort: 30 hours

When and where: See Schedule

Result: 15 minutes presentation of your project notebook. Pleaes upload or link to your notebook to Ilias before presenting (naming convention: surname-firstname-projectname.ipynb)

Teamwork: You are encouraged to work and present in teams of two (or three).

Expectations: Training and study of an autoencoder or a small reinforcement learning project.  

Assessment: You will get feedback 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 module attendance yield 2 ECTS credit points.
Registration: Here on Ilias

More information about the hotel in Mallorca: https://www.esblaudesnord.com/en/. We have pre-booked the single rooms already. You are free to bring familiy and friends of course (not participating in the school) and book extended days on own cost, if there are bigger rooms free.
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 heading the Data Science Lab of the University of Bern. Beyond science he likes philosophical conversations in the evening. 

Dr. Aris Marcolongo 

Aris is an ML expert by training, PhD in computer science, and experience in various research enterprises. Currently he is pursuing a research project investigating ML methods for understatnding extreme climate events with compound drivers. He carries the rare combination of friendliness and deep technical knowledge. 

Dr. Mykhailo Vladymyrov

Mykhailo is a trained physicist who we learned to know at the Albert Einstein Institute of Fundamental physics (and beyond) with many years of experience with big data, machine learning and GPU computing. This year he is working for the Theodor Kocher Institute at the University of Bern. Mykhailo has a high level humor and view upon the human strive. 

Dr. Lorenzo Brigato
Lorenzo is working for ARTORG at the University of Bern. He supposed to have experience with reinforcement learning.