Traditionally, machine learning community has been focused on supervised learning where the source of learning is fully labeled examples including both input features and corresponding output labels. As one way to alleviate the costly effort of collecting fully labeled examples, semi-supervised learning usually concentrates on utilizing a large amount of unlabeled examples together with a relatively small number of fully labeled examples to build better classifiers. Even though many semi-supervised learning algorithms are able to take advantage of unlabeled examples, there is a significant amount of effort in designing good models, features, kernels, and similarity functions. In this dissertation, we focus on semi-supervised learning with partially labeled examples. Partially labeled data can be viewed as a trade-off between fully labeled data and unlabeled data, which can provide additional discriminative information in comparison to unlabeled data and requires less human effort to collect than fully labeled data. In our setting of semi-supervised learning with partially labeled examples, the learning method is provided with a large amount of partially labeled examples and is usually augmented with a relatively small set of fully labeled examples. Our main goal is to integrate partially labeled examples into the conventional learning framework, i.e. to build a more accurate classifier. The dissertation addresses four different semi-supervised learning problems in presence of partially labeled examples. In addition, we summarize general principles for the semi-supervised learning with partially labeled examples.