Detailed Course Information |
2025 Fall
Dec 10, 2025 |
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| ENER 471 - Machine Learning for Energy Systems |
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This course is designed to build industry-valued knowledge for engineering students looking to work in both renewable and non-renewable energy sectors or continue their education in related fields. It is aiming at fostering students knowledge about fundamental theory and algorithms of machine learning as a new trend in the education and in order to evolve in the world’s new direction of science. Concepts such as data science, big-data analytics, data mining, data filtering, probabilistic and statistical analysis and modelling, supervised and unsupervised learning in widely applicable language such as Python, commonly used AI methods and applications, as well as optimization techniques with the main focus on energy systems engineering will be covered.
***Prerequisite: ENER 305 and CS 110:***
0.000 TO 3.000 Credit hours 0.000 TO 3.000 Lecture hours 0.000 TO 6.000 Lab hours 0.000 TO 6.000 Other hours Levels: Undergraduate Schedule Types: Lecture, Lab, Independent Study, Project, Seminar, Directed Reading, Examination *Energy Systems Engineering Department Restrictions: Must be enrolled in one of the following Levels: Undergraduate Graduate |
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