| _version_ | 1866901125347672064 |
|---|---|
| author | Singelmann, Lauren Covarrubias, Jacob |
| author_facet | Singelmann, Lauren Covarrubias, Jacob |
| contents | As machine learning and artificial intelligence become increasingly prevalent in our day-to-day lives, there becomes an even greater need for literacy in machine learning for those outside of the computer science domain. This work proposes a conceptual framework for teaching machine learning to engineering students with the goal of developing the knowledge and skills needed to apply machine learning techniques to engineering problems. Many machine learning courses in computer science, math, and statistics focus on the theoretical basis of machine learning algorithms and assessment. This framework takes a fundamentally different approach by creating a course structure for machine learning practitioners rather than machine learning developers. The presented framework breaks machine learning into four fundamental principles that should be used in any machine learning solution: data (what information we can use to develop our solution), task (what we are trying to accomplish with our solution), algorithms (what computational models we are using to create our solution), and assessment (how we are measuring the success of our solution). To teach this framework, the structure of the course includes creating concept maps of the four fundamental principles and relevant topics, completing coding tutorials, and creating in-class presentations that use and apply the four fundamental principles. The paper will present the need for machine learning and artificial intelligence education within engineering, the framework and supporting learning theory, suggested activities for implementation, and lessons learned from the implementation of this framework in a 1-credit course for engineering students. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_21427_3Z16-YM65 |
| institution | Zenodo |
| language | |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Framework For Teaching Machine Learning For Engineers Singelmann, Lauren Covarrubias, Jacob As machine learning and artificial intelligence become increasingly prevalent in our day-to-day lives, there becomes an even greater need for literacy in machine learning for those outside of the computer science domain. This work proposes a conceptual framework for teaching machine learning to engineering students with the goal of developing the knowledge and skills needed to apply machine learning techniques to engineering problems. Many machine learning courses in computer science, math, and statistics focus on the theoretical basis of machine learning algorithms and assessment. This framework takes a fundamentally different approach by creating a course structure for machine learning practitioners rather than machine learning developers. The presented framework breaks machine learning into four fundamental principles that should be used in any machine learning solution: data (what information we can use to develop our solution), task (what we are trying to accomplish with our solution), algorithms (what computational models we are using to create our solution), and assessment (how we are measuring the success of our solution). To teach this framework, the structure of the course includes creating concept maps of the four fundamental principles and relevant topics, completing coding tutorials, and creating in-class presentations that use and apply the four fundamental principles. The paper will present the need for machine learning and artificial intelligence education within engineering, the framework and supporting learning theory, suggested activities for implementation, and lessons learned from the implementation of this framework in a 1-credit course for engineering students. |
| title | A Framework For Teaching Machine Learning For Engineers |
| url | https://doi.org/10.21427/3Z16-YM65 |