In my teaching, I leverage multidisciplinary knowledge to guide students from diverse academic backgrounds. Through the Hopkins Engineering Applications & Research Tutorials (HEART) program, I developed 10 weeks of comprehensive materials for a new undergraduate course, Decision Making in Power Systems: An Introduction through Machine Learning. This course introduces machine learning techniques to improve decision-making in power system operations and market dynamics through lectures and hands-on learning. I also guide students in developing research projects and data strategies, fostering their analytical and critical thinking skills. The course attracts students from fields such as applied mathematics, electrical engineering, system engineering, and environmental science, equipping them with the skills to contribute to sustainable energy systems. [syllabus]
- Lecture 1: Course Introduction [slides]
- Lecture 2: Introduction to Machine Learning [slides]
- Lecture 3: Linear Regression and Classification [slides]
- Lecture 4: Model Selection and Neural Networks [slides]
- Lecture 5: Feature Selection and Extraction [slides]
- Lecture 6: Clustering Methods and Pattern Analysis [slides]
- Lecture 7: (Guest Lecture) Optimization and Machine Learning [slides by Pietro Favaro]
- Lecture 8: Machine Learning in Power Markets [slides]
- Lecture 9: Trustworthy AI in Power Systems [slides]
- Lecture 10: Course Wrap-up and Student Presentations
Zhirui Liang 