SoSe 24: Current research topics in visual and data-centric computing
Tim Conrad
Comments
In this course, you will learn the basic statistical and algorithmic concepts in Machine Learning, focusing on their practical applications in bioinformatics. You will have the opportunity to work on practical problems and implement and use the methods learned during lectures to analyze biological datasets, drawing examples and case studies from research conducted at the Zuse Institute Berlin, specifically from the department of Visual and Data-centric Computing. This department specializes in developing tools for extracting insights from various datasets across fields including biology, biophysics, neuroscience, and medicine.
We will cover topics like data pre-processing, model implementations, and analysis methods. You will learn about models for regression, clustering, and classification, feature selection, and advanced data preprocessing, such as imputation. We will also cover Deep Learning approaches.
Throughout the course, you will complete weekly exercises with a focus of implementation and data analysis, including written reports. These exercises are designed to reinforce the practical applications of the material covered in the lectures.
By the end of the course, you will be able to process data, choose appropriate models to answer specific questions, evaluate results, and effectively communicate your findings.
14 Class schedule
Regular appointments
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