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Summary form only given. This tutorial is devoted to an important segment of statistical learning techniques related to the problem of supervised learning, which aims at predicting the value of an outcome given a number of inputs. Theoretical material is oriented mainly towards methods and concepts. The introduction outlines general aspects of statistical learning, together with motivations for its applications in medicine and genomics. The second part deals with the main theoretical aspects of supervised learning, including a short overview of statistical decision theory, with the emphasis on the problem of trade-off between bias and variance. Attention is further paid to linear methods, applied to both regression and classification problems. In the presentation of neural networks applied to statistical learning, stress is placed on multi-layer perceptrons and training algorithms based on gradient search techniques. Various issues important in practice are given considerable attention, including cross-validation techniques and the choice of suitable learning procedures.