I think the confusion comes from the fact that normal and Gaussian are synonyms in many (but not all) contexts relating to probability theory. People then confuse normal and normalized, which leads to statements such as those in the videos.
To answer your question: no, many successful researchers in ML are computer scientists or engineers with no rigorous understanding of probability theory and statistics.
Fabulous-Nobody- t1_it2g1um wrote
Reply to [D] is a strong background in math/stats/cs in a necessary condition for becoming a renowned researcher in the ML community? *A passive rant* by [deleted]
I think the confusion comes from the fact that normal and Gaussian are synonyms in many (but not all) contexts relating to probability theory. People then confuse normal and normalized, which leads to statements such as those in the videos.
To answer your question: no, many successful researchers in ML are computer scientists or engineers with no rigorous understanding of probability theory and statistics.