Teaching

Teaching Philosophy

My teaching philosophy centers on two goals for my students: a rigorous theoretical understanding and the ability to implement and apply material to real-world problems. In my experiences as both teacher and student, I have found that the correct balance between theory and application is crucial for learning in any discipline, but particularly important for computational sciences. Each of the two perspectives heightens the other, bringing the students to a more complete understanding of the material and greater proficiency in application. These aims permeate all pedagogical tasks, including curricula and assignments, guiding classroom experiences, and interacting with individual students.

As instructors, we have the power in our classrooms to choose to attend explicitly to issues of access, inclusiveness, fairness and equity.

All teaching evaluations are now public and you can find them here: gatorevals.aa.ufl.edu/public-results/

Course Listings

The courses that I regularly teach are:

• EEL 3850 – Data Science for ECE (former EEL 4930), 4 credits, syllabus
  • Prerequisites: MAC2312 (Calculus 2) and EEL3834 (Programming I)
• EEE 3773 – Introduction to Machine Learning, 4 credits, syllabus
  • Prerequisites: EEL3135 (Signals and Systems)
• EEE 4773 – Fundamentals of Machine Learning, 3 credits, syllabus
  • Prerequisites: EEL3135 (Signals and Systems) and EEL3850 (Data Science for ECE)
• EEL 5840 – Fundamentals of Machine Learning, 3 credits, syllabus
  • Prerequisites: None. Expected: foundational knowledge in probability theory, statistics, linear algebra, multivariate Calculus and programming (Python preferred)
• EEL 4930/EEL – 5934 Applied Machine Learning Systems, 3 credits, syllabus
  • Prerequisites: None. Expected: basic probability, statistics, linear algebra and programming with Python.

Learn more about the different project topics developed in my Machine Learning courses: AI Course Showcase - Project Topic Examples