The session covered several example applications of the Kalman filter and parameter estimation via maximum likelihood. Participants presented their own problems and ideas. This was the final session of the workshop.
[Read More]Welcome to “Introduction to recursive machine learning algorithms”
| Duration: | 3 days (~ 24 h) |
| When: | 3 days on September 2023, 9:00 - 17:15 |
| Where: | OST Campus Rapperswil-Jona, Room TBA |
| Language: | English |
| Participants: | 10 |
| Fee: | 960 CHF |
| Topics: | Machine learning, online algorithms, Kalman filters |
| Requirements: | English, linear algebra (basic), probability theory (basic), programming experience (beginner), Calculus (integrals and derivatives, basic) |
Registration
Please fill this form to register for the workshop. The registration fee to cover the costs of the workshop is of 960 CHF. An invoice will be issued when the workshop starts.
If you complete the workshop you will receive a certificate stating your participation and the details of the workshop.
Navigating this website
The links on the top right will take you to different pages with detailed information about the workshop including the planned timeline, an overview of the contents, and some references that will be useful during the workshop. When they are ready, the link Lectures will link to notes and materials for each lecture or session.
Post on lectures from previous instances of the workshop are found here.
The pages updated most recently are shown below.
Session 2. Learning
The session covered the update step of the algorithm as shown in the sessions structure.
[Read More]Session 1. Modelling
The session covered the prediction step of the algorithm as shown in the sessions structure.
[Read More]Session 3. Applications
The session covered several example applications of the Kalman filter and parameter estimation via maximum likelihood. Participants presented their own problems and ideas. This was the final session of the workshop.
[Read More]Session 2. Learning
The session covered the update step of the algorithm as shown in the sessions structure.
[Read More]Session 1. Modelling
The session covered the prediction step of the algorithm as shown in the sessions structure.
[Read More]