Symbol Kurs

2023-01-17 - 2023-01-20 Machine Learning for Time Series

For CAS AML participants as elective module 6


  • Machine Learning with Time Series (TS)

    In this module we study and practise techniques for doing forecasting, i.e. machine learning on time series.

    Possible learning objectives
    - TS data - univariate and multivariate
    - TS with classical / statistical methods
    - TS with classical machine learning methods 
    - TS with RNN (deep learning)
    - TS with LSTM (deep learning)
    - TS witth CNN (deep learning)
    - TS with Transformers (deep learning)
    - TS with ...

  • Target groug
    CAS AML, 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
  • Python knowledge and programming experience
  • Experience with machine learning and in particular with neural networks

  • Talks, tutorials (with Jupyter notebooks on colab), project work with presentation

  • 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.

  • The coaches are local and external experts
  • Readings
    Forecasting: Principles and Practice -
Time : 2023-01-17 - 2023-01-20 
Location : HG 117 (Tue, Wed, Thu), HG 104 (Fri)
URL: Zoom Link will appear here

Language: English
Participants : Max 20
Registration : Mandatory
Responsible : PD Dr. Sigve Haug
Lectures require voluntary lecturers, if not found they become tutorials

Tentative Schedule

09:00 - 09:45 Introduction to Time Series (TS) - Lecture 1
10:00 - 10:30 Tutorial 1 TS with TF
10:30 - 11:00 Break
11:00 - 11:15 Discussion
11:15 - 12:15 Tutorial 1 TS with TF
12:15 - 12:30 Discussion

09:00 - 09:45 Traditional TS Models - Lecture 2
10:00 - 10:30 Tutorial
10:30 - 11:00 Break
11:00 - 12:30 Tutorial

09:00 - 09:45 TS with Deep Learning - Lecture 3
10:00 - 10:30 Tutorial
10:30 - 11:00 Break
11:00 - 12:30 Tutorial

09:00 - 09:45 TS with Transformers - Lecture 4
10:00 - 10:30 Tutorial
10:30 - 11:00 Break
11:00 - 11:30 Tutorial / discussion session  

Presentation day to be defined during module.
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.
PD Dr. Sigve Haug

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.