I’m a CS student graduating next year, my education has been pretty crap, due to personal problems, and my school curriculum being pretty bad (we never met the requisite depth in any of the courses we did, and skimmed over a lot). After graduation, I want to take a few years (I’m thinking 3 – 6) off to do a lot of self study.
 
I’m not sure how my knowledge level compares to international standards, so just assume no prior CS knowledge (I’ll skip things I already know satisfactorily, but I don’t expect there to be anything I know deep enough that it would be worth skipping it completely). For mathematics, I am at highschool level (currently learning algebra and logic in my free time) sans calculus (which I never really learned), with a little discrete maths. I have no prior philosophy training, and it I sufficient to assume that the entirety of my philosophy knowledge is from Lesswrong.
 
I have a (set of) goals I want to achieve, and I want to learn the required computer science (among other things) in order to achieve my goal. I plan on pursuing a postgraduate degree towards that goal after my gap years (I intend to start producing original research in at most ten years, and most likely much earlier than that).
 

Goals:

1. Formalise learning: Develop a model of learning (the way we have a model of computation). What does it mean for a learning algorithm to be better than another. Develop a method for analysing (I’m thinking of asymptotic analysis (at least as of now, all analysis I plan to do should be asymptotic)) (and comparing) the performance of learning algorithms on a particular problem, across a particular problem class, and across problem space using a particular knowledge representation system (KRS), using various KRS, and across the space of possible KRS. Develop a hierarchy of learning algorithms. Bonus develop a provably optimal learning algorithm.
    
2. Formalise knowledge: Develop a model of a KRS. Develop a method for quantifying and measuring “knowledge”. Develop a method for analysing and comparing KRS, using a particular learning algorithm, using various types of learning algorithms, and across the space of learning algorithms, on a particular problem, across a particular problem class, and across problem space. Develop a hierarchy of KRS. Synthesise the results of the above, and on work on formalising learning into a theory of knowledge (“knowledge theory”). Bonus: Develop a provably optimal KRS.
    
3. Formalise Intelligence: Develop a model of intelligence, A method for quantifying and measuring intelligence of arbitrary agents in agent space. Understanding intelligence and what makes certain agent designs produce more intelligent agents. A hierarchy of intelligent agents. Is there a limit to intelligence? Bonus: Develop a provably optimal intelligent agent.

NB

“Develop” doesn’t mean that one doesn’t already exist, more that I plan to improve on already existing models, or if needed build one from scratch. The aim is model that is satisfactorily (for a very high criteria for satisfy) useful (I think our models of computation are satisfactorily useful. The end goal is a theory that can be implemented to build HLMI). I don’t plan to (needlessly) reinvent the wheel. When I set out to pursue my goal of formalising intelligence, I would build on the work of others in the area).
 
How much CS/Maths/Analytical Philosophy/other relevant subject areas do I need to learn, what areas of CS/Maths/Analytical Philosophy/other relevant subject areas should I focus on, and how deep do I need to go? I want to prepare a complete curriculum for myself. I’d appreciate links to learning resources and recommended books, but I would also appreciate mere pointers.
 
I’m not familiar with the research in this area, I’m vaguely aware that AIXI is along the lines of what I want to do (haven’t yet read it), but not much beyond that. I don’t know what has and hasn’t been done, I don’t know what counts as reinventing the wheel. My aim is to do for Intelligence what Turing (and Church) did for computation .
 
Please help me.

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I’m currently trying to formulate a research project as part of my undergraduate graduation requirement. I’ve always had an interest in AI research, and recently I’ve also developed interest in topics related to logic in computation (SAT solvers and such are neat). As such I was hoping to try and develop some sort of project that would explore AI as it relates to logic, but I’ve been having some difficulty finding recent research in such topics.

Is heavy use of logic more or less obsolete in modern AI research? It seems like machine learning is by far the most prominent field of AI research these days, is this just because of all the hype surrounding ML or is it because other approaches have been dead ends?

In the case that trying to come up with a research project relating logic to AI isn’t a dead end; do you have any suggestions of recent literature about logic in AI or useful vocabulary that would help me find such literature? (My knowledge about logic in computer science isn’t very extensive, so I’m suspecting that I just don’t know enough relevant topics/subfields to find relevant research…)

I am currenly studying Civil Engineering, but i want to learn ML because it fascinates me. As a third year eng student, the maths doesnt bother me to much.

I’ve just started working through Automate the Boring Stuff, to learn python. But should i finish this book, or should i just sign up to something like Dataquest and learn as i go? My concern is that it isn’t really aimed at people who want to get into ML so how much of the knowledge is relevant. I also plan on doing Andrew Ng’s course in the future, but just want to get some opinions on the best path to learn and how much conventional programming knowledge is required.

Also what are your thoughts on the MIT 6.001 and 6.002 courses for someone who’s new to programming and interested in machine learning Thanks guys