Machine Learning
Supervised learning, regression, classification, clustering, dimensionality reduction, neural networks, model evaluation, and practical Python workflows.
Teaching Portfolio
Courses & Teaching Experience
Workshop
Machine Learning
Teaching materials covering core machine learning workflows, including data preprocessing, regression, classification, clustering, dimensionality reduction, neural networks, model evaluation, and explainable AI foundations.
Open Teaching Materials →
ML
Python-based course materials for practical machine learning instruction.
Courses
Teaching Areas
AI Programming Using Python
Python fundamentals, data preprocessing, machine learning workflows, model training, and explainable AI concepts for graduate students.
Expert Systems
Rule-based reasoning, knowledge representation, inference mechanisms, and foundations of symbolic AI systems.
Data Structures
Core programming structures, algorithmic thinking, lists, stacks, queues, trees, graphs, and implementation-oriented problem solving.
Operating Systems
Process management, memory management, scheduling, file systems, and operating-system fundamentals for computer science students.
Experience
Teaching, Instruction & Mentoring
Mar.–May 2023
Instructor — AI Programming Using Python
National Yunlin University of Science and Technology, Taiwan
- Taught Python, preprocessing, machine learning workflows, model training, and XAI concepts.
- Delivered instruction to 30 master's students.
2015–2023
Teaching Assistant — Machine Learning and CS Courses
Shahrood University of Technology / YunTech
- Assisted Machine Learning in 2015, 2016, 2022, and 2023.
- Supported Expert Systems, Data Structures, and Operating Systems courses.
- Mentored students in coding, experiment design, model evaluation, and academic writing.