Perhaps some topics are simply too big to really teach effectively through lectures. You need to go digging on your own to understand all the myriad details, and put in the hours alone with a textbook.
At universities that offer PhDs, being a professor forces you to develop a strong theoretical foundation, which makes it easier to grasp the theory of related subjects. Example: If you have a deep understanding of math/statistics theory, it's much easier to understand a paper on machine learning, even if you're not a computer science professor.
Professors at research oriented universities supervise PhD research students and each student tends to be a multiplier on their supervisor's knowledge since each PhD is a collection and extension of an existing research area. A good PhD student is basically a massive funnel of information.
I'll also say, don't ascribe more abilities or skills to a professor than what you see in the presentation. Sometimes having a strong theoretical orientation/experience comes at the expense of a practical one. Don't assume that having a strong theoretical foundation in Stats/ML automatically makes you a good Data Scientist or ML Engineer.
At 3:40 - For this topic he actually has has 6 or 7 lectures, 100 pages of notes. But it's compressed to an introduction.
At 7:10 - He states HMM is 2-3 lectures, but he's going to compress it to half a lecture.
Honestly, I had courses like this in grad school. The were typically seminars and usually graded on a curve because people crammed stuff into their brains as quickly as possible and barely understood anything! They were meant to give you a broad coverage of a field, not comprehensively cover any particular set of topics.