A final written report, and an oral presentation

In this course, students will begin by learning about time series data in biology and medicine during
the first week. They will explore data visualization and classical methods for time series analysis,
as well as modern machine learning approaches. In the second week, students will work on their
own projects and present them to their classmates.

By the end of this practical course, students will be able to understand and define various types of
time series data. They know about classical forecasting methods, some modern machine learning
techniques, and how to evaluate and interpret the results of time series analyses.

Voraussetzungen: Grundkenntnisse in der Programmierung mit Python, Grundkenntnisse in Statistik und maschinellem Lernen /
Prerequisites: Basic Python programming skills, basic concepts of statistics and machine learning

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Literatur / Literature:
* Auffarth, Ben. Machine Learning for Time-Series with Python: Forecast, predict, and
detect anomalies with state-of-the-art machine learning methods. Packt Publishing Ltd,
2021. https://cdn.supo.vn/timsach/ebooks/9ecd381fcc7fc6a5abf1062fe950a83d/cc42ea53abd3a
b48f2bc5f54e6a29c23/machine-learning-time-series-python.pdf
* Huang, C., & Petukhina, A. (2022). Applied time series analysis and forecasting with
Python. Cham: Springer. https://link.springer.com/book/10.1007/978-3-031-13584-
2?source=shoppingads&locale=de&gad_source=5&gad_campaignid=18594249545&