Abstract : The topics addressed in this thesis lie in statistical machine learning. Our main framework is the prediction of arbitrary deterministic sequences (or individual sequences). It includes online learning tasks for which we cannot make any stochasticity assumption on the data to be predicted, which requires robust methods. In this work, we analyze several connections between the theory of individual sequences and the classical statistical setting, e.g., the regression model with fixed or random design, where stochastic assumptions are made. These two frameworks benefit from one another: some statistical methods can be adapted to the online learning setting to satisfy deterministic performance guarantees. Conversely, some individual-sequence techniques are useful to tune the parameters of a statistical method and to get risk bounds that are adaptive to the unknown variance. We study such connections for several connected problems: high-dimensional online linear regression under a sparsity scenario (with an application to the stochastic setting), online linear regression on L1-balls, and aggregation of nonlinear models in a model selection framework (regression on a fixed design). We also use and develop stochastic techniques to compute the minimax rates of game-theoretic online measures of performance (e.g., internal and swap regrets) in a deterministic or stochastic environment.