It's interesting that MITx is being explicit that their courses will be hard. This provides an interesting market differentiation in the online learning space. The recent Stanford classes & Udacity seem to be aiming for a much more general audience.
That's not true. The Stanford AI class up front said that it required a lot of math. I ended up dropping out because I couldn't catch up quickly enough with the math requirement to complete the course as well.
So for those of us who would like to take this course eventually but don't necessarily have the appropriate math experience in, oh, the last decade or so, are there resources online (or even by book) which would give adequate preparation in a reasonable timeframe?
The class was fairly hard if the material was new to you. However, the only math was probability. If you review basic probability you should be just fine.
Finally made an account on HackerNews just for this post. The class is definitely worth taking, especially if you'd like to have a better understanding of Fourier Transformations.
Are Fourier Transforms covered in 6.002x? I took 6.002 many years ago, and transforms (Fourier Transforms, Laplace Transforms, Z-Transforms, etc.) were covered in 6.003 (Signals and Systems), not much in 6.002 (Intro to Circuits).
Nowadays, you get a (sizable) glimpse of it in 6.02, which is usually taken before 6.003 (in fact, it is a prerequisite), and which deals with digital communication systems.
Perhaps 6.002 also presents pieces of Fourier stuff when needed.
I'm nearing completion of an EE at UT Austin, and this MIT class seems to condense 2 or 3 different semesters of material. I can't decide if this is a good approach or not.
On one hand, you take one broad class that introduces you to many different topics. Then you delve into whatever is necessary for your degree.
On the other, you do deeper (but still introductory-level) examinations of each topic, still with the option to go deeper if you desire.
Ultimately, I don't think either is a clear winner. I don't remember enough out of my intro circuits class to do a thing with a second-order circuit without reviewing theory for a bit first.
The mini-bio of Gerald Sussman on the right-hand side states that "Structure and Interpretation of Computer Programs .. is universally acknowledged as one of the top ten textbooks in computer science".
I'm curious why they explicitely speak about the top ten.
This is a misunderstanding. You can read my question as "According to [..] the SICP is one of the top 10 CS books. Which, do you think, are those?"
I'm just interested because it seems SICP is THE book on programming, and I wonder: Which other CS books are that prominent? The only one I can think of, is "Artificial Intelligence - A Modern Approach" by Russell and Norvig.
This is probably going to devolve again into a long discussion, but here's the books with an academic bent that I was most impressed with (many of them 25 years ago, of course):
"Compilers: Principles, Techniques, and Tools" by Alfred V. Aho, Ravi Sethi and Jeffrey D. Ullman (a.k.a the "Dragon Book")
"The Art of Computer Programming" by Donald Knuth
"Introduction to Automata Theory, Languages and Computation" by John E. Hopcroft and Jeffrey D. Ullman
An amazing book, despite the fact that the core thesis of the first edition, that we were on the verge of permanent world domination by RISC architectures, turned out to be dead wrong:
"Computer Architecture: A Quantitative Approach" John L. Hennessy and David A. Patterson
Two books by Niklaus Wirth (A bit out of fashion, maybe because they were written in the "wrong" languages, and maybe because they were TOO concise in today's world of shovelware books. Wirth is the Strunk & White of CS writers):
"Algorithms and Data Structures" by Niklaus Wirth
"Compiler Construction" by Niklaus Wirth
This book might be the one that impressed me most in my undergraduate studies, although I can't say I've done much with what I read there:
"Parallel Program Design: A Foundation" by K. Mani Chandy and Jayadev Misra
The interesting part about this class is that it is a lab class, yet it is trying to be accessible to anyone and everyone around the world. It will be interesting to see the logistics of how the MITx people handle grading, forums, homework, questions, and all the hard systems problems when 6.002x finally gets underway.
The other question is how will this compare to 6.002 as it is currently taught at MIT - will 6.002x be at the same standard? You as a student in 6.002x won't have the same chance to interact with your classmates as in a traditional MIT course, so can it be considered the same type of class? This all remains to be seen.
