A one-page, curated roadmap for learning machine learning from scratch. It lists the best free resources in the order I'd suggest working through them — the probability, linear-algebra, and programming prerequisites first, then core machine-learning and deep-learning courses, and finally how to move into projects and research. Put together by the lab for students starting out; please feel free to share it, with acknowledgement.

An openly available course (ME 5374) by Prof. Laurent Lessard, Northeastern University. It teaches how to turn real engineering problems into optimization models — linear, quadratic, convex, and mixed-integer / non-convex programs — solve them with modern tools, and interpret the results through sensitivity and trade-off analysis. Lecture slides, Julia notebooks, and project examples are all public. This is an external resource shared here for self-study; full credit to the author.