Ask HN: Older textbooks/papers you consider classics still worth studying today?

It's full of beautiful renderings and diagrams, covers the core algorithms of 2D and 3D graphics, introduces the mathematics required, and many other related subjects such as user interface design.

Apparently there is a 3rd edition from 2013 which looks at modern GPU-based rendering, though I don't own a copy.

https://en.wikipedia.org/wiki/Computer_Graphics:_Principles_...

https://mitpress.mit.edu/sites/default/files/sicp/index.html

"The Mythical Man Month" (1975) - because human nature hasn't changed [https://www.amazon.com/Mythical-Man-Month-Software-Engineeri...]

"The History of Fortran I, II, and III" (1979) - because this historical piece by the author of the first high level language brings home the core principles of language design [https://archive.org/details/history-of-fortran]

"The Unix Programming Environment" (1984) - because the core basics of the command line haven't changed [https://www.amazon.com/Unix-Programming-Environment-Prentice...]

"Reflections on Trusting Trust" (1984) - because the basic concepts of software security haven't changed [https://www.archive.ece.cmu.edu/~ganger/712.fall02/papers/p7...]

"The Rise of Worse is Better" (1991) - because many of the tradeoffs to be made when designing systems haven't changed [https://www.jwz.org/doc/worse-is-better.html]

"The Art of Doing Science and Engineering: Learning to learn" (1996) - because the core principles that drive innovation haven't changed [https://www.youtube.com/playlist?list=PL2FF649D0C4407B30] [https://www.amazon.com/Art-Doing-Science-Engineering-Learnin...]

"xv6" (an x86 version of Lion's Commentary, 1996) - because core OS concepts haven't changed [https://pdos.csail.mit.edu/6.828/2011/xv6/xv6-rev6.pdf] [https://pdos.csail.mit.edu/6.828/2014/xv6/book-rev8.pdf]

Sipser's _Intro to the theory of computation_ (1996; 3e in print)

Aho, Sethi, & Ullman's _Compilers: principles, techniques, and tools_ - 'the dragon book' - (1986; 2e in print)

Various authors' _Handbook of theoretical CS_ (2 volumes, 1990-1)

From information theory:

  A mathematical theory of communication (information theory, Claude E. Shannon) [1]
  Three approaches to the quantitative definition of information (A. N. Kolmogorov) [2]

  A formal theory of inductive inference (Ray Solomonoff, 1964) [3]
  Language identification in the limit (Mark E. Gold, 1967) [4]
  Inductive Inference of formal languages from positive data (Dana Angluin, 1980) [5]
  A theory of the learnable (PAC learning, Leslie Valiant, 1984) [6]
  Occam's Razor (Blumer et al, 1987) [7]

  Long Short-Term Memory (Hochreiter and Schmidhuber, 1997)

The second batch of papers start with Solomonoff's inductive inference papers, kinda important if you want to learn things from other things. Mark Gold's paper proves that it is impossible to learn a non-finite automaton from examples. Dana Angluin's follow up extends this with learnability results about various classes of CFG. Any time someone claims that their deep neural net has learned a CFG, point them to these two papers.

Valiant's paper is the theroy of machine learning as we know it today. It basically relaxes the assumptions made in inductive inference and introduces the notion of error. If you can't learn some concept perfectly, what degree of error is likely from some set of training data? Blumer's paper discusses a further bound on that amount of error that follows an Occamist bias (simplest truths are better) and is a basis for understanding overfitting (error increases as the hypothesis space does).

These two sets of papers probably look disconnected - but, learning is compression. Compression, with generalisation, I guess. Anyway, no, they're not unrelated.

The final paper is the one that introduced LSTMs and the, er, "constant error carousel" (the solution to vanishing gradients, which this paper is worth reading for).

These are papers that one must read if they're interested in machine learning. Carefully so. They're not even "old" papers- more like, essential ones.

I'm totally omitting a whole bunch of others, obviously.

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Online pdfs (not all free):

[1] http://www.math.harvard.edu/~ctm/home/text/others/shannon/en...

[2] http://alexander.shen.free.fr/library/Kolmogorov65_Three-App...

[3.1] https://www.sciencedirect.com/science/article/pii/S001999586... (Part 1)

[3.2] https://www.sciencedirect.com/science/article/pii/S001999586...

[4] https://www.sciencedirect.com/science/article/pii/S001999586...

[6] http://www-personal.umich.edu/~yinw/papers/Angluin80.pdf

[7] https://www.sciencedirect.com/science/article/pii/0020019087...