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Dear visitor,

Please note that this OpenCourseWare platform will be replaced at the end of 2026 by a new platform (under the same url ocw.tudelft.nl).

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0.1.3 Meet your teachers

Course subject(s) Module 00. Welcome to Supervised Machine Learning! Module 01. Introduction to Supervised Machine Learning

Dr. Tom Viering is an assistant professor with a focus on education in the Pattern Recognition and Bio-Informatics research group in the Faculty of Electrical Engineering, Mathematics & Computer Science at the TU Delft.

He is one of the coordinators and teachers in TU Delft’s AI minor. In this program engineers with various backgrounds learn the basics of AI and machine learning and apply the learned techniques in the field of their major.

In his teaching he likes to employ interactive Python widgets to stimulate students’ understanding. His research focusses on theoretical and empirical aspects of machine learning, for example on questions like how much data is necessary for learning.

Hanne Kekkonen

Hanne Kekkonen obtained her PhD in applied mathematics at the University of Helsinki in 2016. After graduation she continued her research on statistical inverse problems as a research fellow at the University of Warwick and University of Cambridge.

Hanne joined the Delft Institute of Applied Mathematics at the Faculty of Electrical Engineering, Mathematics & Computer Science as an assistant professor in 2020. Her research focuses on Bayesian inverse problems, Bayesian statistics and nonparametric statistics.

Research interests:

    • Inverse problems
    • Bayesian statistics
    • Nonparametric statistics
Creative Commons License
AI skills for Engineers: Supervised Machine Learning by TU Delft OpenCourseWare is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://online-learning.tudelft.nl/courses/ai-skills-for-engineers-supervised-machine-learning/
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