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Stellar magnetic fields, as a crucial component of star formation and evolution, evade direct observation at least with current and near future instruments. However investigating whether magnetic fields are generated by a dynamo process or represent relics from the formation process, or whether they show a behavior similar to the sun or something very different, it is essential to investigate their structure and temporal evolution. Fortunately nature provides us with the possibility to indirectly observe surface topologies on distant stars by means of Doppler shift and polarization of light, though not without its challenges. Based on the mentioned effects, the so called Zeeman-Doppler Imaging technique is a powerful method to retrieve magnetic fields from rapid rotating stars based on measurements of spectropolarimetric observations in terms of Stokes profiles. In recent years, a large number of stellar magnetic field distributions could be reconstructed by Zeeman-Doppler Imaging (ZDI). However, the implementation of this method often relies on many approximations because, as an inversion method, it entails enormous computational requirements. The aim of this thesis is to develop methods for a ZDI, designed to invert time-resolved spectropolarimetric data of active late type stars, and to account for the expected complex and small scale magnetic fields on these stars. In order to reliably reconstruct the detailed field orientation and strength, the inversion method is employed to be able to use of all four Stokes components. Furthermore it is based on fully polarized radiative transfer calculations to account for the intricate interplay between temperature and magnetic field. Finally, the application of a newly developed ZDI code to Stokes I and V observations of II Pegasi (short: II Peg) was supposed to deliver the first magnetic surface maps for this highly active star. To accomplish the high computational burden of a radiative transfer based ZDI, we developed a novel approximation method to speed up the inversion process. It is based on Principal Component Analysis and Artificial Neural Networks. The latter approximate the functional mapping between atmospheric parameters and the corresponding local Stokes profiles. Inverse problems, as we are dealing with, are potentially ill-posed and require a regularization method. We propose a new regularization scheme, which implements a local entropy function that accounts for the peculiarities of the reconstruction of localized magnetic fields. To deal with the relatively large noise that is always present in polarimetric data, we developed a multi-line denoising technique based on Principal Component Analysis. In contrast to other multi-line techniques that extract from a large number of spectral lines a sort of mean profile, this method allows to extract individual spectral lines and thus allows for an inversion on the basis of specific lines. All these methods are incorporated in our newly developed ZDI code iMap, which is based on a conjugated gradient method. An in depth validation of our new synthesis method demonstrates the reliability and accuracy of this approach as well as a gain in computation time by almost three orders of magnitude relative to the conventional radiative transfer calculations. We investigated the influence of the different Stokes components (IV / IVQU) on the ability to reconstruct a known synthetic field configuration. In doing so we validate the capability of our inversion code, and we also assess limitations of magnetic field inversions in general. In a first application to II Peg, a K2 IV subgiant, we derived temperature and magnetic field surface distributions from spectropolarimetric data obtained in 2004 and 2007. It gives for the first time simultaneously the temporal evolution of the surface temperature and magnetic field distribution on II Peg.