this book is an introduction to the mathematical description of information in science and engineering. the necessary ma- thematical theory will be treated in a more vivid way than in the usual theoretical proof structure. this enables the reader to develop an idea of the connections between diffe- rent information measures and to understand the trains of thoughts in their derivation. as there exist a great number of different possible ways to describe information, these measures are presented in a coherent manner. some examples of the information measures examined are: shannon informati- on, applied in coding theory; akaike information criterion, used in system identification to determine auto-regressive models and in neural networks to identify the number of neu- rons; and cramer-rao bound or fisher information, describing the minimal variances achieved by unbiased estimators.