Most passive Machine Learning tasks can be (re)stated as sequence prediction problems. This includes pattern recognition, classification, time-series forecasting, and others. Moreover, the understanding of passive intelligence also serves as a basis for active learning and decision making. In the recent past, rich theories for sequence prediction have been developed, and this is still an ongoing process. On the other hand, we are arriving at the stage where some important results are already termed classical. While much of the current Learning Theory is formulated under the assumption of independent and identically distributed (i.i.d.) observations, this lecture series focusses on situations without this prerequisite (e.g. weather or stock-market time-series).