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2020-09-28 - 2020-10-02 Bern Autumn School on Advanced Machine Learning 2020

Deep Neural Networks - Advanced Topics

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About
Bern Autumn School on Machine Learning
Module 3 of the CAS Advanced Machine Learning

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 school you attend lectures and tutorial sessions over four mornings. This happens in a great hotel on Mallorca, a great resort for hiking and water sport.

Learning outcomes, participants will
  • TBD
Target group
  • Enrolled people for CAS Advanced Machine Learning
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 experiences
  • The training is as language independent as possible, but examples and practical work is in Python
Methods
  • Lectures based on Jupyter notebooks, evening talks, tutorials (with Jupyter notebooks), project work with presentation.
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 : 2020-09-28 - 2020-10-02 (afternoons for work, swimming, hiking, wellness or whatever) 
Location : https://www.esblaudesnord.com/
Course Fee : Included in the CAS fee 

Language: English
Participants : Max 24
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Schedule
Monday 2020-09-28 (Arrival)
20:00 - 21:00 Dinner at the hotel
 
Tuesday 2020-09-29
08:00 - 08:45 TBD 
09:00 - 09:45 TBD
09:45 - 10:15 Coffee break
10:15 - 11:30 TBD
11:45 - 12:30 TBD
12:15 - 17:00 Beach, sport, hiking, work or whatever
17:00 - 19:00 Tutorial
20:00 - 20:00 Dinner at hotel
20:00 - 20:30 TBD

Wednesday 2020-09-30
08:00 - 08:45 TBD 
09:00 - 09:45 TBD
09:45 - 10:15 Coffee break
10:15 - 11:30 TBD
11:45 - 12:30 TBD
12:15 - 17:00 Beach, sport, hiking, work or whatever
17:00 - 19:00 Tutorial
20:00 - 20:00 Dinner at hotel
20:00 - 20:30 TBD

Thursday 2020-10-01
08:00 - 08:45 TBD 
09:00 - 09:45 TBD
09:45 - 10:15 Coffee break
10:15 - 11:30 TBD
11:45 - 12:30 TBD
12:15 - 17:00 Beach, sport, hiking, work or whatever
17:00 - 19:00 Tutorial
20:00 - 20:00 Dinner at hotel
20:00 - 20:30 TBD

Friday 2019-10-02
08:00 - 08:45 TBD 
09:00 - 09:45 TBD
09:45 - 10:15 Coffee break
10:15 - 11:30 TBD
11:45 - 12:30 TBD
12:30         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 (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.


Presentations date TBD
Location: TBD


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: Here on Ilias

Additionally you have to book a room at the hotel www.esblaudesnord.com (included in the CAS fee). 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 rooms free.

Travel:             We may arrange for transport from Palma Airpoort (45 minutes). More information will come.
Readings
You will profit a lot more from the school if you read a bit up front in these online resources  (google it)
  • The school content is largely based on the last part of 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 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. 

Dr. Radhakrishna Achanta 

Radhakrishna is a senior data scientist at the Swiss Data Science Center. He has that good entrepreneur orientation. https://datascience.ch/team_member/radhakrishna-achanta/

Dr. Mykhailo Vladymyrov (tutorials)

Mykhailo is the best tutor we ever had. He is a trained physicist who 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.