Throughout my career, I’ve had the pleasure of teaching university students in a variety of roles, including lecturing on object-oriented programming in C++, supervising robotics course projects, and leading a machine learning course. This page provides a selection of the resources I created and developed for these teaching efforts.
Introduction to Machine Learning & Pattern Recognition
During the Summer 2022 semesters at Northeastern University, I served as a part-time lecturer for the Electrical & Computer Engineering department’s “Introduction to Machine Learning & Pattern Recognition” course. The course covered a broad range of fundamental machine learning algorithms, emphasizing the underlying models behind each approach to help students understand where, how, and why these methods work. It balanced theoretical foundations with practical applications to prepare students for a future career in machine learning.
The following are a subset of lecture slides I created and used for the course, which were accompanied by a complementary Git codebase containing additional course resources.
- Course Overview & Basics
- Foundations:
- Introductory Algorithms:
Robot Operating System (ROS) Tutorials
When I was PhD student, I was a teaching assistant for the “Human-Centered Robotics” course in the Electrical & Electronic Engineering department at Imperial College London. As part of my role, I organized and delivered a tutorial on the Robot Operating System (ROS).
The Git repositories supporting this tutorial are listed below:
- Main tutorial package, including the lecture slides
- Toy ROS package to teleoperate a P3-AT robot
I would also like to acknowledge the colleagues who helped prepare the tutorial content. Their names are listed in the lecture slides within docs of the main tutorial package.