60 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2026-10-supervised-machine-learning-using-python DSC-2025-24 | Safe, FAIR, Legal: Working with Personal Data in Research de Universität Bremen Thu, 20 Aug 2026 19:48:41 +0200 Thu, 20 Aug 2026 19:48:41 +0200 Universität Bremen content-739765 Wed, 12 Aug 2026 00:11:28 +0200 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2026-10-supervised-machine-learning-using-python#c739765 <p>Machine learning is increasingly used to analyze research data, make predictions, and support decisions. Supervised learning is one of its central paradigms and is used whenever a model learns from data for which the outcome is already known. It can, for example, be applied to predict numerical measurements, classify observations, estimate the probability of an event, or identify patterns associated with a particular outcome.</p> <p>Applications include understanding which factors help explain environmental variables, identifying biological or medical samples, estimating health or social outcomes, detecting known faults in technical systems, and forecasting economic indicators. Successfully applying machine learning requires understanding how models learn from data, how their performance should be evaluated, and whether their predictions are reliable and meaningful, in addition to technical programming skills such as Python.</p> <p>This workshop combines the theoretical foundations of supervised learning with practical implementation in Python. Through interactive lectures and hands-on exercises, participants will learn how classical algorithms such as linear, logistic, and flexible regression models, K-Nearest Neighbours, and ensemble methods, including Random Forest, work, when to use them, and how to evaluate their performance on real-world datasets. The concepts covered also provide the foundation and baseline models needed for working critically with advanced approaches such as neural networks.</p> Content content-739763 Sun, 16 Aug 2026 20:01:17 +0200 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2026-10-supervised-machine-learning-using-python#c739763 <p>The course deals with the following topics:</p> <ul><li data-list-item-id="e6f4965ee4d43209980ea1adfe2953185">Understand the fundamental concepts and workflow of supervised learning</li><li data-list-item-id="e67ab540dc7b294046ea2ee41a1e05c2f">Distinguish between regression and classification problems</li><li data-list-item-id="e246957ddaf621f45efd2ab79da6c22f2">Understand and implement and evaluate linear, logistic, and flexible regression models for predictive modeling and binary classification tasks</li><li data-list-item-id="e80f99219c3e3d28b95c0ece887f411c6">Understand and implement K-Nearest Neighbours and ensemble methods, including Random Forest</li><li data-list-item-id="e0f3b4562216e68419a900f98104e3fed">Compare the strengths and limitations of different supervised learning algorithms and evaluate models using appropriate performance metrics</li><li data-list-item-id="ef1fff953ffb4be6d0a357d90a84f95c3">Gain practical experience training, evaluating, and comparing models in Python</li></ul> <p>The workshop begins with an introduction to supervised learning concepts before progressing to classical machine learning algorithms for regression and classification. Participants will apply the methods to real-world datasets and learn how to select and critically evaluate suitable models for different research questions.</p> Content content-739761 Sun, 16 Aug 2026 20:02:10 +0200 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2026-10-supervised-machine-learning-using-python#c739761 <h5>DAY 1 Foundations of Supervised Learning: </h5> <p><strong>a) Part 1: Introduction to Supervised Learning</strong><br />   • What is machine learning? <br />   • Supervised vs. unsupervised learning <br />   • Training, validation, and test sets <br />   • Regression vs. classification problems <br />   • Overview of the Machine Learning workflow <br />   • Practical: Setting up the Python environment and exploring datasets <br /><strong>b) Part 2: Linear Regression and Linear Classification</strong><br />   • Intuition behind linear regression <br />   • Cost function and least squares <br />   • Model training and prediction <br />   • Model evaluation metrics for regression (MAE, MSE, RMSE, R²) <br />   • Limitations of linear relationships and introduction to Generalized Additive Models (GAMs)<br />   • From regression to classification, linear classification concepts, and linear decision boundaries <br />   • Practical: Implementing linear, and flexible regression and linear classification models in Python</p> <h5>DAY 2: Classification Algorithms and Ensemble Learning:</h5> <p><strong>c) Part 3: Logistic Regression</strong><br />   • Limitations of linear classification<br />   • Introduction to logistic regression <br />   • The sigmoid function <br />   • Probability estimation and decision thresholds <br />   • Evaluating classification models (accuracy, precision, recall, F1-score) <br />   • Practical: Binary classification with logistic regression in Python<br /><strong>d) Part 4: K-Nearest Neighbors and Ensemble Learning</strong><br />   • Instance-based learning <br />   • Introduction to K-Nearest Neighbors (KNN)<br />   • Advantages and limitations of KNN <br />   • Introduction to ensemble learning <br />   • Voting, Bagging, and Boosting Concepts <br />   • Decision trees and Random Forests<br />   • Practices of model comparison<br />   • Practical: Comparing multiple supervised learning models in Python</p> Content content-739757 Wed, 12 Aug 2026 00:19:53 +0200 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2026-10-supervised-machine-learning-using-python#c739757 <p>This workshop is intended for researchers from <strong>all disciplines</strong> who wish to build a solid foundation in supervised machine learning. <strong>Basic Python programming skills</strong> are required but can be acquired through introductory self-study materials, such as the Data Science Center’s GitHub repository “<a class="external-link" href="https://github.com/Data-Science-Center-UB/Python-Introduction-for-Researchers" target="_blank">Python Introduction for Researchers</a>”. <strong>Familiarity with basic mathematical and statistical concepts</strong>, such as equations, means, variance, and correlations, is also expected. No prior experience with machine learning models is required.</p> Content content-739755 Sun, 16 Aug 2026 20:05:20 +0200 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2026-10-supervised-machine-learning-using-python#c739755 <ul><li data-list-item-id="ef2f670a7ddf0611ab32e93ffc3f0b75e">Own laptop and connection to the Wifi (e.g. via <a class="external-link" href="https://www.uni-bremen.de/en/dezernat8/netzbetrieb/wifi/overview-wifi" target="_blank">eduroam</a>).</li><li data-list-item-id="e50e80168af6947d1d3149548fca41197">Please make sure you have access to the <a class="external-link" href="https://nfdi-jupyter.de/" target="_blank">Jupyter4NFDI</a>.</li></ul> Content content-739749 Wed, 12 Aug 2026 00:20:50 +0200 https://www.uni-bremen.de/data-science-center/trainings-services/trainings-workshops/dsc-2026-10-supervised-machine-learning-using-python#c739749 Content