I'd heard horror stories of other ML professors at Columbia, but Satyen turned out to be a dream. I believe he used Daniel Hsu's syllabus and materials throughout the course, but his style was much different. While Daniel focuses heavily on the math and the proofs and (I would assume) hope that intuition on the mechanisms of the ML models follows, Satyen would focus heavily on the visuals and step-by-step walkthroughs of everything before touching on the math. He hadn't taught before, but I feel like coming from industry was a boon because he seemed comfortable going through the same concepts a couple times to make sure everyone got it. He managed to make almost everything (even kernels!) readily digestible, and made me feel confident in being to implement models later in the homeworks.
My only gripe with him was the mathematical rigor of the exams. He lifted them straight from Daniel, who spends _his_ classes delving into math, so the dissonance between Satyen's style and the first midterm was jarring. When students pointed this out on Piazza, he made an effort to include more proof-based questions on the homeworks to give us exposure to the type of questions we might expect on the final. I still didn't do too well on the final, but neither did anyone else.