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Good question. I've been teaching myself the requisite maths for about a year and a half now and am finally getting where I can understand/apply most ML papers
by jfaucett 10y ago
Good question. I've been teaching myself the requisite maths for about a year and a half now and am finally getting where I can understand/apply most ML papers and ideas.
I don't know what level you're at right now, I was pretty far behind and had to spend a good deal of time with the basics to make sure I really understood them i.e. algebra, trigonometry and calculus and proofs, etc.
After that though I'd say the following are must know areas:
1. Linear Algebra
2. Probability Theory
3. Statistics
4. Optimization Theory
5. Signal Processing (Information Theory) Fourier Transforms, etc.
But the good thing is once you understand those fields pretty well, applying the methods and solving problems using them is really not that difficult.
- majkinetor 10y agoSo above 5 domains make you able to read research papers in general and apply information there to your case ?
- jfaucett 10y agoI'd say for about 95% of it yes. Other fields can be useful, Graph Theory for instance when you are analyzing graphs i.e networks and stuff, and complexity theory when you are analyzing and comparing different algorithm implementations. But those 5 above are the ones you just keep seeing/using over and over and over.
- rahimnathwani 10y ago"I've been teaching myself the requisite maths for about a year and a half now and am finally getting where I can understand/apply most ML papers and ideas." This sounds awesome and fast. I'm curious to know: 1) What background did you have at the start? 2) What resources did you find most valuable, and how did you use them? (e.g. did you just get a book per topic, and work through chapter-by-chapter, doing the exercises?) 3) Did you get help from friends/colleages, or were you able to work through all the materials independently?