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Lawrence C. Paulson

ML for the Working Programmer, 2nd Edition 2nd Edition

by L. C. Paulson (Author)

4.6 23 ratings

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The new edition of this successful and established textbook retains its two original intentions of explaining how to program in the ML language, and teaching the fundamentals of functional programming. The major change is the early and prominent coverage of modules, which the author extensively uses throughout. In addition, Paulson has totally rewritten the first chapter to make the book more accessible to students who have no experience of programming languages. The author describes the main features of new Standard Library for the revised version of ML, and gives many new examples, e.g. polynomial arithmetic and new ways of treating priority queues. Finally he has completely updated the references. Dr. Paulson has extensive practical experience of ML, and has stressed its use as a tool for software engineering; the book contains many useful pieces of code, which are freely available (via Internet) from the author. He shows how to use lists, trees, higher-order functions and infinite data structures. He includes many illustrative and practical examples, covering sorting, matrix operations, and polynomial arithmetic. He describes efficient functional implementations of arrays, queues, and priority queues. Larger examples include a general top-down parser, a lambda-calculus reducer and a theorem prover. A chapter is devoted to formal reasoning about functional programs. The combination of careful explanation and practical advice will ensure that this textbook continues to be the preferred text for many courses on ML for students at all levels.

ISBN-10

052156543X
ISBN-13

978-0521565431
Edition

2nd
Publisher

Cambridge University Press
Publication date

June 28, 1996
Language

English
Dimensions

6.69 x 1.13 x 9.61 inches
Print length

500 pages
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Editorial Reviews

Review

"The book is an excellent introduction to ML, but even better, it provides a good overview of functional programming." Jeffrey Putnam, Computing Review

Book Description

This new edition of a successful text treats modules in more depth, and covers the revision of ML language.

Product details

Publisher ‏ : ‎ Cambridge University Press; 2nd edition (June 28, 1996)
Language ‏ : ‎ English
Paperback ‏ : ‎ 500 pages
ISBN-10 ‏ : ‎ 052156543X
ISBN-13 ‏ : ‎ 978-0521565431
Item Weight ‏ : ‎ 1.79 pounds
Dimensions ‏ : ‎ 6.69 x 1.13 x 9.61 inches

Best Sellers Rank: #1,650,657 in Books (See Top 100 in Books)
- #2,175 in Software Development (Books)
- #5,433 in Programming Languages (Books)
- #6,060 in Computer Software (Books)

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4.6 23 ratings

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23 global ratings

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Tatiana Racheva

Wanted to understand OCaml functors

Reviewed in the United States on June 28, 2022

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I have some experience with OCaml and was looking for additional resources to learn more about modules and functors. The author does a really good job showing how and why you would want to use functors, which was a mystery to me even after a few years of using the language. In general, I found 100% of the contents of this book applicable to OCaml in the year 2022, even though it concerns Standard ML and hails from the 90s. There are a few very minor differences, but they don't get in the way of trying examples and exercises.

The book is full of simple and elegant code, and it helped me understand better how to write idiomatic ML.

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Daniel Lyons

A special, important, idiosyncratic book

Reviewed in the United States on June 25, 2011

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I bought this book and simultaneously Elements of ML Programming, ML97 Edition (2nd Edition) . This book, ML for the Working Programmer, is a pretty unique book. It strikes me as a very personal book. The author seems to be involved in theorem proving, which probably explains why a chapter is devoted to the subject and another chapter is devoted to implementing the lambda calculus. I wouldn't normally consider these things working programmers are interested in, but it's easy to cut Paulson some slack because he's an easy writer. While the tone is precise most of the time, the author just can't hold back a ripping joke or a snide remark every once in a while. It's a charming book.

Of all the ML books I've seen (which is most of them) this seems to be the winner. I found Ullman's book a bit too compressed. For example, I wanted to see more material on the module system and functors; Paulson delivers, Ullman left me wanting. Ullman is very, "here's the syntax, here are the semantics." Paulson is much more, "here's three examples of what I am talking about, let's discuss the nuances." Both books spend a great deal of time discussing functional programming. I came into Standard ML from Haskell, so I found a lot of that material old hat, but again, I cut slack because these books are not new anymore but the language was fairly new when they were written. Functional programming techniques were very new and most people didn't have much exposure to them. If you are new to functional programming, I'm sure it won't disappoint.

If you're setting out to learn Standard ML, I think this is a great book with more of a tutorial feel than Ullman's. Also more depth in some areas, like modules. Then again, I like concise books too; I wouldn't say Ullman's is a bad book, just not as good for my purposes.

If you already know functional programming, you will probably want to skip a chapter or two. Particularly if you already know Haskell, you will probably find it very hard to get worked up over maps and folds. If your interest in ML is really an interest in the cutting edge of functional programming or type theory, this book is probably more of a historical curiosity, and you will probably get more out of something like Real World Haskell or Types and Programming Languages .

If you're shopping for a programming language, let me say that Standard ML is a language with few proponents these days. But, unlike most languages that are not widely used, there are four or five well-known, stable and mature compilers and interpreters available for Standard ML, for free. Because it is so perfectly defined, it isn't going anywhere while you aren't looking. It's a safe investment. Also, it is easier to learn than Haskell. There's fewer syntax rules (albeit more ceremony), but it's more familiar and more regular, easier to learn. Also, the runtime semantics are less weird because it is not lazily evaluated. On the other hand, Haskell really seems to be going places these days. If you are being strictly practical or strictly theoretical, the investment in Haskell is more likely to pay dividends and I'd get Real World Haskell. But if you give it a shot, you may find yourself charmed by this ugly duckling of a language and its quirky caretakers.

46 people found this helpful

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Peter

A Good Book at a Good Price

Reviewed in the United States on November 7, 2019

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The paperback covers had both been folded and unfolded, but the
contents were in perfect condition. It was an excellent price for this
expensive text book.

One person found this helpful

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kapilash

A very Good book on ML and programming in general

Reviewed in the United States on June 5, 2014

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ML is a very well designed programming language. And a direct consequence of this is that if you understand the principles behind its design, we get to really master the language. ML for the working programmer teaches you those principles.

While mastery of ML is by itself a great deal, the examples provided and the expositions there of help you become better at functional programming in particular and programming in general. The third and fourth chapters were eye-opening for me in understanding the full power of lists and trees. The chapters that follow just go on to become even better.

Do yourself a favor and invest your money and time in this extremely well written book.

5 people found this helpful

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King Yin Yan

the best!!

Reviewed in the United States on June 19, 2010

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A great book, by the creator of the theorem provers Cambridge LCF and Isabelle/HOL.

ML was a language created by Robin Milner who had the ingenious idea of building LCF in ML and using ML's type system to ensure that theorems proved in LCF will always be secure.

This book explains programming in ML with an emphasis on building theorem provers, covering topics like lambda calculus.

The last chapter explains the full implementation of a simple theorem prover similar to LCF. This is invaluable to those who'd like to understand LCF, HOL, Isabelle, HOL Light, etc.

10 people found this helpful

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Mike

Well written book

Reviewed in the United States on May 12, 2015

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This is a very well written book for SML. If you're interested in OCAML or F# this is a good book to get because SML is the core of these languages.

6 people found this helpful

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A Student

The past into the future?

Reviewed in the United States on March 22, 2008

My interest in learning ML started with reading the writings of people like Paul Graham who extoll the virtues of functional programming. ML seemed like the most accessible language for someone coming from an imperative oop background (due to the absence of '(' ... ')' which permeate Lisp and Scheme). There is however a dearth of introductory material on the web and what is out there seems to offer a piece meal, fragmentary overview. So I picked up this book and was not disappointed.

Paulson does an excellent job of introducing ML concepts in a clear logical manner. This book is about a lot more than ML though. Paulson teaches functional programming in this book with ML as the vehicle. This is a great book for self study. So why not five stars? The typesetting is horrendous. This is not a pretty book.

I think pretty much everyone will admit that ML never gained a lot of traction (Ocaml a bit more than SML I believe). The main problem I see with using ML for a large project is the lack of library support. So why learn ML? It turns out that ML has had an influence on new languages that have come out in recent years; F# and Scala are two. So time spent with ML should pay off when exploring these newer languages and whose close association with the .Net and Java platforms (respectively) cures the library availability dilemma.

16 people found this helpful

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receivefreedom

Ausführlich und super erklärt

Reviewed in Germany on January 15, 2018

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Wer das Programmieren neu lernen möchte, kann dies sehr gut mit diesem Buch tun.
Jedoch ist das Buch auch für viele interessant, die bereits programmieren können.
ML (Meta Language) ist eine funktionale Programmiersprache, die wirklich gut erklärt wird. Wenn man zuvor noch nie funktional programmiert hat, kann man aus diesem Buch so einiges mitnehmen.
Jedoch denke ich auch, dass Leute, die bereits funktionale Programmierung beherrschen, ebenfalls einiges aus diesem Buch lernen können, da es sehr umfassend ist.

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