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ML for the Working Programmer
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ML for the Working Programmer

2 Revised edition

Book Details

Format Paperback / Softback
ISBN-10 052156543X
ISBN-13 9780521565431
Edition 2 Revised edition
Publisher Cambridge University Press
Imprint Cambridge University Press
Country of Manufacture GB
Country of Publication GB
Publication Date Jun 28th, 1996
Print length 500 Pages
Weight 852 grams
Dimensions 24.50 x 16.90 x 2.70 cms
Ksh 12,000.00
Manufactured on Demand 0 in stock

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The major change for the new edition of the successful text is the extensive use of modules. In addition, the first chapter has been totally rewritten to make the book more accessible to first year students. The main features of new Standard Library for the revised version of ML are described, and many new examples are given.
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 are extensively used throughout. In addition, the first chapter has been totally rewritten to make the book more accessible to those without experience of programming languages. The main features of new Standard Library for the revised version of ML are described and many new examples are given, while references have also been updated. 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 the Internet) from the author. He shows how to use lists, trees, higher-order functions and infinite data structures. Many illustrative and practical examples are included.. Efficient functional implementations of arrays, queues, priority queues, etc. are described. Larger examples include a general top-down parser, a lambda-calculus reducer and a theorem prover. The combination of careful explanation and practical advice will ensure that this textbook continues to be the preferred text for many courses on ML.

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