Machine Learning Modules for All Disciplines
Autor: | Mary-Angela Papalaskari, Carol Weiss, Thomas Way, Yamini Praveena Tella, Lillian N. Cassel, Paula Matuszek |
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Rok vydání: | 2017 |
Předmět: |
Class (computer programming)
Computer science business.industry 4. Education 05 social sciences Big data 050301 education Nature of Science 02 engineering and technology Machine learning computer.software_genre Data science Robot learning Learning sciences Variety (cybernetics) World Wide Web 020204 information systems Active learning 0202 electrical engineering electronic engineering information engineering Artificial intelligence business 0503 education computer |
Zdroj: | ITiCSE |
Popis: | Recognizing that the changing nature of science and its reliance on massive amounts of data has led to the integral use of machine learning approaches in just about every discipline, we present the results of a multi-year research effort entitled "Broader and Earlier Access to Machine Learning." For this project, we explored teaching strategies for introducing machine learning topics to non-technical students in discipline-relevant ways, culminating in a large collection of ready-to-use learning modules suitable for use in a wide variety of academic fields. We present a roadmap to our online repository of module materials, a detailed walk-thru of the contents of an example module, ideas and approaches for incorporating modules into a class or assisting non-technical colleagues in doing the same, and a summary of results of using these modules in course settings. |
Databáze: | OpenAIRE |
Externí odkaz: |