Teaching ML - researchr conference series publications

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2022

Developing Open Source Educational Resources for Machine Learning and Data ScienceLudwig Bothmann, Sven Strickroth, Giuseppe Casalicchio, David Rügamer, Marius Lindauer, Fabian Scheipl, Bernd Bischl. teachml 2022: 1-6 [doi]

Introduction to AI and its medical applications: Crash Course for an audience with diverse scientific backgroundsDonatella Cea, Helene Hoffmann, Marie Piraud. teachml 2022: 7-11 [doi]

Teaching Machine Learning with Applied Interdisciplinary Real World ProjectsGulustan Dogan. teachml 2022: 12-15 [doi]

Hearts Gym: Learning Reinforcement Learning as a Team EventJan Ebert, Danimir T. Doncevic, Ramona Kloß, Stefan Kesselheim. teachml 2022: 16-21 [doi]

Stimulating student engagement with an AI board game tournamentKen Hasselmann, Quentin Lurkin. teachml 2022: 22-26 [doi]

Will the sun shine? - An accessible dataset for teaching machine learning and deep learningFlorian Huber, Erica Dafne van Kuppevelt, Peter Steinbach, Colin Sauze, Yang Liu, Berend Weel. teachml 2022: 27-31 [doi]

The Third Teaching Machine Learning and Artificial Intelligence Workshop, 19-23 September 2022, Grenoble, France and onlineKatherine M. Kinnaird, Peter Steinbach, Oliver Guhr, editors, Volume 207 of Proceedings of Machine Learning Research, PMLR, 2022. [doi]

A Deep Learning Bootcamp for Engineering & Management StudentsLukas Lodes, Alexander Schiendorfer. teachml 2022: 32-36 [doi]

Data, Trees, and Forests - Decision Tree Learning in K-12 EducationTilman Michaeli, Stefan Seegerer, Lennard Kerber, Ralf Romeike. teachml 2022: 37-41 [doi]

Teaching Machine Learning with mlr3 using ShinyGero Szepannek, Laurens Martin Tetzlaff, Alexander Frahm, Karsten Lübke. teachml 2022: 42-45 [doi]

Machine Learning Students Overfit to OverfittingMatias Valdenegro-Toro, Matthia Sabatelli. teachml 2022: 46-51 [doi]

2021

Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challengesViviana Acquaviva. teachml 2021: 35-39 [doi]

Participatory Live Coding and Learning-Centered Assessment in Programming for Data ScienceSarah M. Brown. teachml 2021: 73-77 [doi]

Teaching Deep Learning, a boisterous ever-evolving fieldAlfredo Canziani. teachml 2021: 30-34 [doi]

Putting the "Machine" Back in Machine Learning for Engineering StudentsTing-Wu Chin, Dimitrios Stamoulis, Diana Marculescu. teachml 2021: 78-82 [doi]

Teaching Machine Learning in the Context of Critical Quantitative Information LiteracyCarrie Diaz Eaton. teachml 2021: 51-56 [doi]

Staying Ahead in the MOOC-Era by Teaching Innovative AI CoursesPatrick O. Glauner. teachml 2021: 5-9 [doi]

Teaching ML in 2021 - An Overview and IntroductionOliver Guhr, Katherine M. Kinnaird, Peter Steinbach. teachml 2021: 1-4 [doi]

Deep Learning Projects from a Regional Council: An Experience ReportJónathan Heras. teachml 2021: 15-19 [doi]

Teaching machine learning through end-to-end decision makingHussain Kazmi. teachml 2021: 10-14 [doi]

Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, September 8+13, 2021, Virtual ConferenceKatherine M. Kinnaird, Peter Steinbach, Oliver Guhr, editors, Volume 170 of Proceedings of Machine Learning Research, PMLR, 2021. [doi]

Using Matchboxes to Teach the Basics of Machine Learning: an Analysis of (Possible) MisconceptionsErik Marx, Thiemo Leonhardt, David Baberowski, Nadine Bergner. teachml 2021: 25-29 [doi]

A lesson for teaching fundamental Machine Learning concepts and skills to molecular biologistsRabea Müller, Akinyemi Mandela Fasemore, Muhammad Elhossary, Konrad U. Förstner. teachml 2021: 68-72 [doi]

Teaching Machine Learning in Argentina: the ClusterAI pipelineMartin Palazzo, Agustin Velazquez, Melisa Breda, Matias Callara, Nicolas Aguirre. teachml 2021: 83-87 [doi]

Deeper Learning By Doing: Integrating Hands-On Research Projects Into A Machine Learning CourseSebastian Raschka. teachml 2021: 46-50 [doi]

Experiences from Teaching Practical Machine Learning Courses to Master's Students with Mixed BackgroundsOmar Shouman, Simon Fuchs, Holger Wittges. teachml 2021: 62-67 [doi]

Teaching Uncertainty Quantification in Machine Learning through Use CasesMatias Valdenegro-Toro. teachml 2021: 57-61 [doi]

An Introduction to AI for GLAMDaniel van Strien, Mark Bell, Nora Rose McGregor, Michael Trizna. teachml 2021: 20-24 [doi]

Teaching Responsible Machine Learning to EngineersHilde Jacoba Petronella Weerts, Mykola Pechenizkiy. teachml 2021: 40-45 [doi]

2020

Proceedings of the First Teaching Machine Learning and Artificial Intelligence Workshop, September 8+14, 2020, Virtual ConferenceBernd Bischl, Oliver Guhr, Heidi Seibold, Peter Steinbach, editors, Volume 141 of Proceedings of Machine Learning Research, PMLR, 2020. [doi]

An Interactive Web Application for Decision Tree LearningMiriam Elia, Carola Gajek, Alexander Schiendorfer, Wolfgang Reif. teachml 2020: 11-16 [doi]

AI is not Just a TechnologyClaudia Engel, Nicole Coleman. teachml 2020: 23-28 [doi]

Introductory Machine Learning for non STEM studentsJavier García-Algarra. teachml 2020: 7-10 [doi]

Teaching the Foundations of Machine Learning with CandyDaniela Huppenkothen, Gwendolyn Eadie. teachml 2020: 29-35 [doi]

Teaching Computational Machine Learning (without Statistics)Katherine M. Kinnaird. teachml 2020: 17-22 [doi]

Turning Software Engineers into Machine Learning EngineersAlexander Schiendorfer, Carola Gajek, Wolfgang Reif. teachml 2020: 36-41 [doi]

Teaching Machine Learning in 2020Peter Steinbach, Heidi Seibold, Oliver Guhr. teachml 2020: 1-6 [doi]

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