Look at the videos from Urmson's talk at SXSW. That shows the worldview of a Google self-driving car. It's about 80% geometry and 20% classification.
Yes, you can get a pure deep learning system to drive on a freeway. But how does it do in a more cluttered environment? A system that builds local maps and profiles terrain with LIDAR can deal with clutter.
I don't want courses that teach math by coding but actual math courses. A lot of these online ML courses treat math as a black box so you never fully understand what's going on & a lot of math based ML courses are too advanced.
So I'd rather focus on understanding the math basics before diving into ML. So what do you guys recommend?
Probability by Joe Blitzstein, Harvard http://projects.iq.harvard.edu/stat110/youtube
And some linear algebra book by Strang (as others have said.) I don't remember which one I learned from, but I do remember it was excellent.
I can't recommend a good stats/probability book. People recommend Statistical Inference by Casella and Berger, although I wasn't super impressed with it.
You'll also want a good understanding of set theory and proof writing. I don't remember what book I learned from, but try to find some introduction to math thought.
This is all assuming you have a good understanding of algebra. The above mentioned books require geometry/trig, but you don't actually need that for machine learning.
Incidentally, my college calculus class also used Stewart.
My philosophy with textbooks was to buy only ones I felt I'd go back to after uni - basic references in each major field I studied - and this was reasonable for me for calculus. My only regret was shelling out for new copies because I was young and stupid and thought I needed them right now for the class, before I knew better. You can certainly pick up cheap second-hand copies per the parent post.
I also used Kreyzig too but found that to be a bit harder to learn from, albeit more in depth.
I learned from the Stewart book and it's not terrible, but I don't think that is a very good book for self learning. How to ace calculus or the calculus lifesaver, plus a book with solved examples is probably better for self study.
Sites like Khan Academy, PatrickJMT, Paul's Online Math Notes, and BetterExplained are useful, too.
Coursera and edx also have offerings.
Probability and statistics will help ML as well.
I would recommend Khan Academy before any other resources, whatever your level. It has online exercises with auto-correction, and a mastering system that uses spaced repetition. This will ensure you'll actually assimilate the material. After you can use another resource as a reference.
The former seems to assume prior knowledge and the latter seems more beginner friendly, except it uses R (in case anyone wants the distinction).
I would like to add www.codingthe matrix.com for any developers, which has a MOOC as well (Coursera), as a complement to Strang.
A lot of the material in this book makes much more sense having had experience coding machine learning algorithms.
Step 2: duct tape a lidar to the top of the car
Step 3: ????
Step 4: enjoy your self driving car
And edX's Intro to Probability (MIT 6.041x) https://www.edx.org/course/introduction-probability-science-... BTW, this course starts again on Jan 17.
I don't know any good MOOC statistics courses. Most that I've seen have oversimplified the concepts into recipes.
After that you probably will have enough domain knowledge to choose what course to take next yourself.
Anyone know of something like that?
Again - what I noted in another comment - if you aren't comfortable with coding (Python and C/C++ in the nanodegree course), or linear algebra - you might have a tough time. You'll also likely want to brush-up on your stats/probability knowledge (though this hasn't come up yet - not like the CS373 course, or the AI and ML Classes). Finally, while you won't absolutely (well - not that I've seen yet) need super-detailed knowledge of what a derivative is or how it is derived (?) - that bit of Calculus seems to be important in machine learning (it is something I struggle with).
Ultimately, give it a shot and have some fun; so far, I have enjoyed everything (just finished the lesson and lab for the Keras framework last night). Don't get too hung-up on the "due dates" for projects; ultimately the main due-date is the end of the term (you have to have everything completed by then to not have to retake the term). Be sure to use the forums, your mentor, the Slack channels, and the facebook page to get help and inspiration. Oh - and you are able to publish your solutions and code around, but if you decide or need to use code provided by someone else (or you use someone else's solution, or ideas from a paper, etc) - YOU NEED TO DOCUMENT THIS in the project rubric (otherwise it will be considered plagiarism which, if found out, may get you kicked from the course) - so be sure to credit your sources!
Finally - if you enjoy the course - be sure to check out Udacity's CS373 course, their "Intro to Artificial Intelligence" course (both of those I believe are free?), and Coursera's Machine Learning course (also free). There are also a ton of other great online resources and books out there; let me know if you need or want suggestions!
I'm not even talking about mathematical rigor, I myself prioritize intuition over symbolic rigor, but you need to have the intuition of what's going on, not treat keras like a black box that magically spits out a ConvNet.
Another thing I dislike is the lack of real world production deployment work. In an engineering oriented course, this should be the top focus. Unfortunately, this isn't really the case.
That being said, I would say it's better than many universities.
They are professionals, so they know "what it takes" to be good at the subject.
Machine learning math is not rigorous in comparison to the type of math you'd study as a math or physics major.
Machine learning math is useful precisely because of the fact that it shows you intuitively how the concepts work.
Knowing the "high level" ideas is near-useless. I can get the "high level" ideas of quantum mechanics from any high school or pop science physics book. Does that mean I know quantum mechanics? Only at an extremely superficial level.
And I suspect anyone taking a MOOC on AI or machine learning wants to end up with more than superficial knowledge.
I was mostly speaking about math professors in college. I have degrees in Math and EE...advance math isn't an issue.
Having abstract intuition (aka "high level ideas") in math is incredibly value.
Sometimes things get hairy in the computation and you need to step back and consider the bigger picture again.
If you don't understand how the math works then you don't have the intuition and vice-vera.
Mind you, I'm kinda biased as I have taken the CS373 course, and I am currently enrolled in and taking the Nanodegree course.
But I am willing to be objective about it. I can honestly say that in both, I have learned some useful things. What I have learned I believe I can build upon myself to explore new areas (for myself - maybe not in the general sense). Are the courses perfect? No.
Both skim rapidly over the material - which can leave you wondering and questioning things. In many cases (labs and projects especially) you need to rely upon asking questions of others - your peers or mentors - in the forums and other outlets (I particularly like the Slack channels in the Nanodegree for this). There's more than a bit of "reading in between the lines" and needing to reference notes and prior code implemented. But that should be expected.
I think both rather expect you to already have a good handle on coding, as well as experience or exposure to linear algebra, stats/probabilities, and some calculus (primarily what a derivative is and how to get one); those last two are sticking points for me (especially the latter - prior courses have at least given me a better grasp and intuition about probability - but I am not an expert at it).
If you don't have those pre-reqs, you're going to have a tough time at progressing meaningfully thru the courses.
I also know that neither course is a substitute for anything like a uni-level CompSci degree, or courses for that, or electrical engineering, mechatronics, machine learning, etc. If you want that kind of rigorous treatment, you're going to need to buckle down and do it.
But you know what? These courses (and a few other things I've read) are inspiring me to pursue a BS, then an MS in CompSci! Which I think is great, even if it's a "late-stage" thing for me to do (I'm 43 years old, and I never went to college - my higher-learning after high school was an "associates degree" from a private tech-oriented vocational school which I am pretty sure is worthless today; I've taken a couple community-college classes, too). I plan to do this all online (I don't have the time or money to do it any other way, unfortunately).
Right now, I'm not sure how these Udacity courses will fit into things, but I'm using them as stepping stones to greater possible efforts. The worst case scenario is that I use what I have learned in my hobby of DIY robotics; I've spent more for less (for instance, a year-long membership to TechShop that I only visited about 3-4 times, because it was located so far away from my house).
So - I guess I am curious about your views and why you think it is "pretty bad"?