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Maybe it's a difference in interpretation, but to me, an 'intensive course' for people who are working is a few weeks of classroom-led instruction. That's the i
by roel_v 11y ago
Maybe it's a difference in interpretation, but to me, an 'intensive course' for people who are working is a few weeks of classroom-led instruction. That's the interpretation I was dismissive of.
Without wanting to make this into a discussion about what 'a productive machine learning guy' needs to know, I would say that you need to be able to understand, implement and calibrate most state of the art machine learning techniques. So then you need 3-4 semesters of undergrad maths (algebra 1 & 2, calculus 1 & 2, statistics, linear algebra), a theoretical machine learning specific course (course as in 'university course', so 8-12 weeks with 1/2 lectures per week plus lab sessions, plus homework/exercises, plus exam; this could maybe be consolidated into half that time for a more experienced person who commits himself 8 hours a day plus half a day on the weekends), an operations research or similar course, and maybe a numerical computing course or short course.
All of this is assuming that the candidate already has a solid grasp of computer programming, including some data structures, algorithms, distributed systems maybe. Plus good practical skills, but that should be the least of the problems.
This is at least one year of full time study, for a gifted person who can really commit to the program. Realistically it's 1 1/2 or 2 years for most people.
Somebody without a college degree will likely not know any of these topics (I mean the things from my second paragraph), save maybe some high school calculus and stats. Maybe they've used R a bit and have done some numerical computing or so. But that's a far way from becoming what I would call 'a productive machine learning guy'.
edit: typo
- blub 11y agoThanks for the thorough answer. My interpetation was that the person described could get away with having fundamental knowledge - this was optimistic and involved too much guessing on my side.
- roel_v 11y agoWell I can't blame you - I have thought for years myself that I could 'pick up machine learning' like it was some web programming framework that you can be an expert at over a weekend. But I had to concede after a few years of trying on and off that it doesn't work like that. Which is also why it's a testy subject for me - like how ex-smokers are usually the most obnoxious anti-smoking crusaders. I don't have a proper math-intensive CS degree myself, only a 'software engineering' degree, so I've been going back to uni and studying when I can to build a math foundation first. So what I wrote down above is basically the 'curriculum' I designed for myself, as well as what I already know from the software side and is needed for ML. To make it relevant again to the topic at hand - this is also why a university degree (and a proper one) is required for programming jobs that are cutting edge. Sure, making websites doesn't require it - and neither do line of business CRUD applications. The interesting work though is not 'programming', it's 'using math to solve problems and using programming to get the computer to do the repetitive stuff for you'. When it's framed like that, it becomes obvious why a university degree is required. (yes yes theoretically it's possible to study math by yourself - the number of people who have the discipline to do that though is minuscule compared to the number of people who can bootstrap themselves to be 'programmers' of simple applications like web applications, most mobile apps or CRUD applications)