Focusing on the fundamentals

By Stefanie Koperniak

When Alex Taffe enrolled in an introductory machine learning course from MITx at MIT Open Learning, he wasn’t just looking to fill a gap in his graduate curriculum — he was reigniting a curiosity first sparked years earlier in the Micro Air Vehicles Club at his alma mater Arizona State University (ASU).

The club was on a mission to build a quad copter that could successfully navigate an obstacle course in a competition. Taffe said that this challenge piqued his interest in the field of computer vision, inspiring him to take a graduate-level course in computer vision at ASU while earning his bachelor’s degree in computer systems engineering.

He worked as a software engineer, including at an avionics company, for five years before pursuing a master’s degree in computer science from the University of California, Davis (UC Davis). ASU hadn’t offered an introductory machine learning course when Taffe was there, and although UC Davis offered a couple of options, they were both undergraduate classes and therefore didn’t have much availability for the enrollment of graduate students and wouldn’t count towards his degree.

After some searching, he found the MITx course 6.036 Introduction to Machine Learning, taught by Leslie Kaelbling, ​Tomás Lozano-Pérez, ​Isaac Chuang, and ​Duane Boning. Part of MIT Open Learning, MITx courses are online educational experiences drawn directly from the MIT classroom and developed in collaboration with MIT instructors. The 6.036 course was designed to teach learners the core principles, algorithms, and applications of machine learning, and also to engage them in exercises of both supervised learning and reinforcement learning.

“I started the MITx class right as I started my master’s degree,” says Taffe. “It has served as a true introductory class for me and prepared me for a machine learning class that I later took at UC Davis.”

While participating in this class, he also watched Professor Tamara Broderick’s 6.036/6.862: Introduction to Machine Learning lectures on YouTube, which he says he also found very useful.

Taffe has worked as a researcher at UC Davis’s Marcu Laboratory, which uses biophotonics to work toward better diagnosis, treatment, and prevention of diseases. He continues to be especially interested in computer vision techniques, and has written Medium articles on topics of real-time instance segmentation and object detection. He also served as a teaching assistant for an Introduction to Programming class. As a teaching assistant, he often thought about the learning process and the best ways to absorb new content. He says he has especially valued the opportunity to be immersed in the content of the MITx course through the homework.

“The assignments were really comprehensive, and that was one thing I felt was missing from my graduate program’s course in machine learning,” says Taffe. “We had the opportunity to really dive into the topics in detail, and the homework really helped me to absorb that foundational knowledge.”

He is currently in the process of job searching after completing his graduate program in June 2025. He says now that he has a little more time, he frequently revisits the course and reviews the content, and says the course has helped prepare him for the next stage of his career.

“The world of AI is very fast-paced and always evolving,” says Taffe. “It’s an advantage to have a mastery of the basics.”

Focusing on the fundamentals was originally published in MIT Open Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.