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The author makes a point that I relate to. I've been on the receiving end of a couple 'Statistical Learning 101' courses. These courses go roughly as he describ
by _RPL5_ 6y ago
The author makes a point that I relate to. I've been on the receiving end of a couple 'Statistical Learning 101' courses. These courses go roughly as he describes it in his blog post: they first teach you how to multiply two matrices, then launch into linear and logistic regression, then classification via clustering, then decision trees and SVMs, then CNNs and deep learning. Along the way, they do a lecture or two on reinforcement learning and HMMs.
In the end, I ended up with a thin smear of half-baked knowledge in my head, where I stop understanding the math once we are half-way through the material.
So how to you achieve this level of deep/intuitive understanding:
> Without understanding the mathematical underpinnings of key models and techniques in full detail, students aren’t able to quickly choose the right models for certain scenarios.
Does anyone have a good study plan with MOOCs and so on? If you have any practical advice, I would appreciate it!
- orange3xchicken 6y agoI mean it really depends how deep you want to go. Like you point out that the classes you took are 101 courses. These are really just "tasting" courses. I'm sure if you decided to take more advanced/grad numbered courses, or unnumbered "topics" courses, you would have a better idea of what's going on. In general, "having a deep understanding of models & techniques in full detail" is not well-defined. For example, analysis of linear regression is often offered as a full year-long sequence for graduate students in math/stats depts. Is this necessary for doing linear regression in practice? Not really, but who cares - it's interesting stuff in its own right. Most people just need just enough understanding to finish a job. In general, the precise medium that you use to study something isn't that important as long as it works for you, but there is a good reason that there are longstanding classic textbooks that people swear by in most fields of mathematics. I do strongly feel that in the context of any mathematical subject, that there are few substitutes to the grind - doing proofs and solving problems on your own. Okay, but just to have at least one link in my post, I want to share this guy MathematicalMonk who used to make really great videos on ml-related stuff: https://www.youtube.com/channel/UCcAtD_VYwcYwVbTdvArsm7w https://www.youtube.com/channel/UCcAtD_VYwcYwVbTdvArsm7w
- _RPL5_ 6y agoThank you!
- ImaCake 6y agoPart of the problem is that math and statistics are poorly taught most of the time. Textbooks will skip crucial steps in their proofs. Math professors will be too lazy to update their course notes based on frustrated student feedback. Courses will lack tutorials and other ways to discuss problems. I shouldn't have any reason to be watching youtube videos or khan academy to fill the gaps. But me, and thousands of others, are forced to do exactly that.