Vitor Lavor , Fernando de Come , Moisés Teles dos Santos , Ardson S. Vianna Jr.
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引用次数: 0
Abstract
A set of hands-on activities, that were proposed in an introduction course to machine learning in a Chemical Engineering undergraduate course, are presented. The activities aimed to introduce basic concepts of unsupervised learning (e.g., clustering) and supervised learning (e.g., classification and regression). Google Colaboratory, a cloud service provided by Google for free to promote research in Artificial Intelligence and Machine Learning, was used to develop these activities, but the proposed activities can be run similarly in a local Python environment. The datasets used in the activities are publicly available on websites such as Kaggle and University of California (UCI), and a specific example in chemical engineering for the ore grinding process was also used. The student's response to the ML topic within the course was very positive.
期刊介绍：
Education for Chemical Engineers was launched in 2006 with a remit to publisheducation research papers, resource reviews and teaching and learning notes. ECE is targeted at chemical engineering academics and educators, discussing the ongoingchanges and development in chemical engineering education. This international title publishes papers from around the world, creating a global network of chemical engineering academics. Papers demonstrating how educational research results can be applied to chemical engineering education are particularly welcome, as are the accounts of research work that brings new perspectives to established principles, highlighting unsolved problems or indicating direction for future research relevant to chemical engineering education. Core topic areas: -Assessment- Accreditation- Curriculum development and transformation- Design- Diversity- Distance education-- E-learning Entrepreneurship programs- Industry-academic linkages- Benchmarking- Lifelong learning- Multidisciplinary programs- Outreach from kindergarten to high school programs- Student recruitment and retention and transition programs- New technology- Problem-based learning- Social responsibility and professionalism- Teamwork- Web-based learning
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