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So we might consider the physical world to be a finite and discrete system. Digital computers are also by definition finite and discrete, but at a much smaller scale than the cosmological universe and coarser than the subatomic level, although modern basic hardware components using nanotechnology are scraping against an atomic scale.

The software that runs on this hardware is essentially a sequence of bits describing how bits are transformed into bits, and hence also this is finite and discrete. So one may say that calculus formulates continuous models as approximation of a discrete reality and numerical analysis simulates these models on computers by producing a discrete approximation at a much coarser level of the continuous models.

To describe the behaviour of a computer, it is clear that it can be in many, but eventually, only a finite number of different states. No wonder that students in computer science will have to learn about discrete mathematics. This involves traditionally graphs, Boolean calculus, complexity, and sometimes also algebraic relations and structures like finite groups, rings, etc.

In the early days of computer science, it was the playground of engineers and mathematicians who simulated their numerical models. Therefore many of the "discrete mathematics courses for computer science" were given to mathematics or engineering students who wanted to specialize in computers. However computer science is now a mature topic in its own right, and students of computer science may not have the traditional mathematical background of previous generations.

This book is taking this observation into account, and starts at a very elementary mathematical level, even explaining proof techniques, while introducing the first concepts of induction, sets, formal logic, The text is largely self-contained. All the required concepts and notations are defined, and all the statements are proved in full.

How much math do you need for Computer Science?

A computer could be used occasionally to solve the exercises but one can perfectly do without. The text has 31 relatively short chapters, each followed by an itemized summary and about a dozen exercises.

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Some of the chapters are related, like for example the ones on probability, but others can be skipped without a problem. The main global topics include formal logic, graphs, automata, and complexity. The pages have a wide outside margin where all the notes and illustrations are included.

To illustrate how the chapters are built up, let's look at the first one which is an illustration of the pigeonhole principle. A simple concept, and yet, it starts with ideas about a mathematical theorem which is all about formulating and proving a general proposition, rather than just a particular case, but it runs up to the fundamental theorem of arithmetic every positive integer is a unique product of prime numbers and the proof that there are infinitely many prime numbers.

Further proof techniques are illustrated with other elementary results from number theory. The book continues with sets, used to introduce relations and functions, and induction lead to countable sets, but also to strings generated from an alphabet using syntax rules. English Deutsch.

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Mathematics for Computer Science revised Monday 5th June, , Eric Lehman. Google Inc. F Thomson Leighton. Department of Mathematics and the. This course covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as.

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