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The professor himself probably agrees with you. In lecture 18 the professor states At 3:40 - For this topic he actually has has 6 or 7 lectures, 100 pages of

by apohn 9y ago

The professor himself probably agrees with you. In lecture 18 the professor states 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.

j9461701 9y ago
Cryptography in undergrad was a course like that at my school. Here is the most basic understanding of group theory we can possibly get away with, here is how it applies to RSA. Next lecture now we're moving on to elliptical curves.... 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.
- adrianN 9y ago
  The point of those lectures, especially in undergrad, is to reduce the "unknown unknowns". You don't know that you could look up how some technique actually works if you haven't ever heard about it. If you want all the details you need good textbooks, or, for more advanced topics, the original papers.
severine 9y ago
How much material have the profs digested in order to make this huge curriculums? Because they're not just listing "anything they've read about the subject", are they?
- apohn 9y ago
  Some professors have had 20+ years to digest an enormous amount of information. Keep in mind they have 2 major advantages over people in industry when learning this stuff. 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.

by apohn 9y ago

j9461701 9y ago

Cryptography in undergrad was a course like that at my school. Here is the most basic understanding of group theory we can possibly get away with, here is how it applies to RSA. Next lecture now we're moving on to elliptical curves.... 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.

adrianN 9y ago
The point of those lectures, especially in undergrad, is to reduce the "unknown unknowns". You don't know that you could look up how some technique actually works if you haven't ever heard about it. If you want all the details you need good textbooks, or, for more advanced topics, the original papers.

severine 9y ago

How much material have the profs digested in order to make this huge curriculums? Because they're not just listing "anything they've read about the subject", are they?

apohn 9y ago
Some professors have had 20+ years to digest an enormous amount of information. Keep in mind they have 2 major advantages over people in industry when learning this stuff. 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.