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Repository for the exercise of Machine Learning for Time Series (MLTS), a lecture given at FAU.

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Machine Learning for Time Series (MLTS) - Exercise

Overview

This repository contains exercises, code, and resources for the Machine Learning for Time Series (MLTS) course at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). The course covers various techniques for analyzing and modeling time series data.

The course is organized by:

Machine Learning and Data Analytics Lab (MaDLab)
Department Artificial Intelligence in Biomedical Engineering (AIBE)
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Links:

Structure

The exercises are structured into six topics, with each topic beeing discussed over two sessions. First you will get a theoretical overview of the topic and a Jupiter Notebook with practical tasks. In the following session, we will discuss and solve the tasks. On StudOn, you will find the slides for the different topics, while the tasks as well as the solutions will be uploaded to this repository.

Requirements

You can find the nessesary requirements in the requirements.txt files of each exercise folder.

Installation

# Clone the repo
git clone https://github.com/username/mlts-exercises.git
cd mlts-exercises

# Setup a virtual environment

# Install your dependencies

License

MIT License

Copyright (c) 2024 Machine Learning and Data Analytics Lab FAU

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Repository for the exercise of Machine Learning for Time Series (MLTS), a lecture given at FAU.

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