Self-Study Questions Thread

If you read a textbook is too hard, you will quickly discover and correct your mistake.

Not in my experience. It's pretty hard to figure out what you don't know.

Original link (at Abstruse Goose).

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Newton gave out reading lists like that too. Geniuses are always the worst teachers.

Real interest rates are negative, so currently, money doesn't grow in value over time. A better investment would probably be to learn skills that you do plan on using.

1. "Probability theory" is an extremely broad term. You don't need any calculus to understand discrete random variables (and if your motivation is directed towards MIRI research, this is mostly the kind of probability that's relevant). You'll want to know some one-variable calculus to understand continuous random variables (so you can manipulate probability density functions and maybe characteristic functions). It would be helpful to be comfortable with some multivariable integral calculus for the purpose of understanding collections of continuous random variables, but something like Stokes' theorem won't be particularly relevant.

2. My impression is that most traditional statistics courses are very bad (not even because of anything to do with frequentism, they're just very bad). Don't take one.

3. Can you clarify what you mean by this?

Traditional statistics classes teach procedures, but not the math needed to understand them. Being told to use maths without being told how the maths work is infuriating to certain sorts of people, and those sorts of people are abnormally concentrated on LessWrong. Jaynes builds up procedures from first principles, which appeals to some but is pointlessly abstract for others.

Different minds learn differently. The answer to this poll entirely depends on who you are.

1. No.
2. I don't know.
3. Yes.

It is worth pointing out that probability and statistics are generally considered to be separate, although closely related topics. Because statistics is built on probability, you are probably better off learning probability first, and then statistics.

I highly recommend the book Concepts, Techniques, and Models of Computer Programming (http://www.amazon.com/Concepts-Techniques-Models-Computer-Programming/dp/0262220695) which is the closest I've seen to distilling programming to its essence. It's language agnostic in the sense that you start with a small "kernel language" and build it up incorporating different concepts as needed.

You'll have to specify your needs a bit better.

Are you interested in algorithms (in which case you'd want to take a look at Knuth)? In the proper way to architect or organize software (in which case Design Patterns might be useful)? In how to write clear, understandable, efficient code (in which case language will have to enter into this)?

I generally recommend Code Complete by Steve McConnel for procedural and OO programming, less so for rapid scripting, even less suitable if you are into functional programming (where SICP rules supreme). For ways to deal with the common issues on a software development team, see his other books, Software Project Survival Guide. Rapid Development is also good.

Completely language-agnostic programming book is a bit of a contradiction. You're better off finding books on the subject that use the language you're most comfortable with. I'd look at books on object oriented programming and algorithm design, and when you're completely comfortable with those topics, move on to design patterns.

What level are you at with programming and what kinds of techniques? Algorithms and data structures? Design patterns?

Skimming through these two textbooks, it seems that:

1) Information Theory... would go down a lot easier if you are already comfortable with probability theory. Nothing fancy, but being able to solve basic "balls and urns" problems, which require some knowledge of combinatorics. Also, knowledge of linear algebra would help a lot.

2) ...Economic Analysis seems to require comfort with calculus. Comfort with reading, manipulating and understanding graphs---similar to the kind you encounter in Newtonian kinematics---would also go a long way.

For introductory math, I've enjoyed the Art of Problem Solving series. I too prefer the slow and thorough method of study, and these books are really comprehensive.

For study motivation, try the Akrasia Tactics thread, as well as these posts.

For myself, I've found that the best study motivator is to attend a class on the subject, even if that means paying for it. In fact, paying for it is an extra motivator to do the necessary work.

I'm actually in a very similar situation to you. Several of the links above were provided by Jonah Sinick over at Cognito Mentoring. I'm in the middle of an email conversation with him and he's been very helpful.

Yes, I have had similar issues and I can't say that I did manage to overcome them successfully, but I'm committed to continue, and I'm in the middle of reorganizing my life so that I could direct more resources there.

I'm not offering any specific advice for now, beside the obvious: http://www.sparringmind.com/changing-habits/, http://www.sparringmind.com/productivity-science/, but I'm responding here to start the dialogue and to nudge us both in the right direction.

So yeah, I wish for you to untangle your motivations and follow through.

Instead, try to figure out why you should care about the many-worlds interpretation.

This strikes me as terrible advice. You're advising people to exclusively read filtered evidence on a topic they know nothing about.

Don't attempt to study machine learning. Pick an outcome you want ... and study enough to get that done.

For the record, machine learning is a topic where (1) it's easy to fool yourself with over-optimistic test result (2) it's easy to waste months improving parts of a project which do not matter in the end . These problems are both mitigated (somewhat) by learning the foundations.

Not to say you wouldn't learn a lot by just diving in. Diving in, then going back and figuring out what you were doing seems like it could be a good thing for many people.

I think your plan is good, and I like the idea of having it visible (and on github as well - a requirement for software engineers these days).

You have 32 hours for HTML and CSS but you could also include some time prior to that on core design principles / Human Computer interfaces. It is one of those skills that while not directly related to building the software (interface is often done by a design team), will set you apart as a developer.

Hey. Your plan looks interesting and I'm wishing you all the best with your project on self-studying. If you want to learn about logic take a look at the online courses on coursera from the University of Melbourne : https://www.coursera.org/course/logic1 https://www.coursera.org/course/logic2

Div, Grad, Curl is a great introduction to vector calculus.

If you're still interested in learning, I found that physics 1 on MIT OCW did a pretty good job of explaining it.

To just learn the operations, this was a good reference. Dot product and cross product is explained about halfway through. here is the accompanying video where Walter Lewin (a charismatic and energetic physics prof) walks through actually doing it.

That was the first CS I read, and was what convinced me to study Computer Science. There are no prerequisites.

EDIT: When I read that book, I had known Scheme) and calculus for several years. I don't recall either of them being required, but (outside view) they might have been. If you don't know how to program at all in any language, you may get lost. But in general that book does a very good job of exposition.

I'd claim that basic programming background, and high school math. Any math in AIMA that goes beyond high school is easily learnable on demand. If you don't know programming things will be difficult.

It would be helpful to be able to program, especially as most of the exercises require it, but it's not neccessary.. A basic grasp of math, particularly logic is also helpful, although the book does introduce them when neccessary.

Can you clarify what you mean by working in AGI? Is your aim, for example, to contribute to MIRI research? In that case, at least at the moment it seems like a good idea to learn some mathematical logic (enough to understand the incompleteness theorem, say) and some basic probability. On the other hand, if your aim is to contribute to creating AGI, then I'm honestly not sure if it's even a good idea on net to offer advice in this direction.