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: https://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
  • Pre-requisites: MAC 2312 Calculus 2, EEL 3834 Programming I

• EEE 3773 – Introduction to Machine Learning, 4 credits
  • Pre-requisites: EEL 3135 Signals and Systems
• EEE 4773 – Fundamentals of Machine Learning, 3 credits
  • Pre-requisites: EEL 3135 Signals and Systems and EEL 3850 Data Science for ECE
• EEL 5840 – Fundamentals of Machine Learning, 3 credits
  • Pre-requisites: None. Expected: foundational knowledge in probability theory, statistics, linear algebra, calculus (preferred calculus III, at least calculus II) and programming (Python preferred but not necessary)