Teaching Experience
Details and material for courses I helped with as a TA.
During my time as a graduate student at Columbia University’s School of Engineering & Applied Science, I had the privilege of being involved in teaching alongside Prof. Zoran Kostic as a teaching assistant for his courses. On this page you will find details about the responsibilities I shouldered and links to selected teaching material I created in this capacity.
EECS E4750: Heterogenous Computing for Signal and Data Processing
| Course Instructor | Semester(s) | Year |
|---|---|---|
| Prof. Zoran Kositc | Fall | 2020 |
Responsibilites
- Course assistance through guided examples and teaching hours.
- Assignment creation: design challenging assignments to test OpenCL and CUDA programming concepts taught for the course.
- Assignment, exam and project grading.
Teaching Material
I do not have permission to expose most of the course material I created for these courses, but the introductory notebooks on OpenCL and CUDA are basic enough that I feel comfortable sharing them here:
ECBM E4040: Neural Networks and Deep Learning
| Course Instructor | Semester(s) | Year |
|---|---|---|
| Prof. Zoran Kositc | Fall | 2020 |
Responsibilites
- Course assistance through guided examples and teaching hours.
- Assignment, exam and project grading.
Misc. Teaching Material
ECBM E6040: Neural Networks and Deep Learning Research
This course was fluidly taught through the medium of regular lectures and guest speakers. Occasionally, students would be tasked with preparing a full lecture on an interesting paper or concept. I contributed with two sessions: a primer on adversarial learning, and a deep dive of the Single-Shot Multi-box Detector (SSD) for object localization/detection.
My notes on the SSD are lost. If I find them, I’ll include them here. The Jupyter Notebook on Adversarial Learning is readily available though: