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ORCID2nd Teaching Machine Learning Workshop 2021, Virtual Conference
- Katherine M. Kinnaird, Peter Steinbach, Oliver Guhr:
Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, September 8+13, 2021, Virtual Conference. Proceedings of Machine Learning Research 170, PMLR 2021 - Oliver Guhr
, Katherine M. Kinnaird, Peter Steinbach:
Teaching ML in 2021 - An Overview and Introduction. 1-4 - Patrick O. Glauner:
Staying Ahead in the MOOC-Era by Teaching Innovative AI Courses. 5-9 - Hussain Kazmi:
Teaching machine learning through end-to-end decision making. 10-14 - Jónathan Heras:
Deep Learning Projects from a Regional Council: An Experience Report. 15-19 - Daniel van Strien, Mark Bell, Nora Rose McGregor, Michael Trizna:
An Introduction to AI for GLAM. 20-24 - Erik Marx, Thiemo Leonhardt, David Baberowski, Nadine Bergner:
Using Matchboxes to Teach the Basics of Machine Learning: an Analysis of (Possible) Misconceptions. 25-29 - Alfredo Canziani:
Teaching Deep Learning, a boisterous ever-evolving field. 30-34 - Viviana Acquaviva:
Teaching Machine Learning for the Physical Sciences: A summary of lessons learned and challenges. 35-39 - Hilde Jacoba Petronella Weerts, Mykola Pechenizkiy:
Teaching Responsible Machine Learning to Engineers. 40-45 - Sebastian Raschka:
Deeper Learning By Doing: Integrating Hands-On Research Projects Into A Machine Learning Course. 46-50 - Carrie Diaz Eaton:
Teaching Machine Learning in the Context of Critical Quantitative Information Literacy. 51-56 - Matias Valdenegro-Toro:
Teaching Uncertainty Quantification in Machine Learning through Use Cases. 57-61 - Omar Shouman, Simon Fuchs, Holger Wittges:
Experiences from Teaching Practical Machine Learning Courses to Master's Students with Mixed Backgrounds. 62-67 - Rabea Müller, Akinyemi Mandela Fasemore, Muhammad Elhossary, Konrad U. Förstner:
A lesson for teaching fundamental Machine Learning concepts and skills to molecular biologists. 68-72 - Sarah M. Brown:
Participatory Live Coding and Learning-Centered Assessment in Programming for Data Science. 73-77 - Ting-Wu Chin, Dimitrios Stamoulis, Diana Marculescu:
Putting the "Machine" Back in Machine Learning for Engineering Students. 78-82 - Martin Palazzo, Agustin Velazquez, Melisa Breda, Matias Callara, Nicolas Aguirre:
Teaching Machine Learning in Argentina: the ClusterAI pipeline. 83-87
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