ABSTRACT: Sequence labeling is a problem of critical importance in many application areas such as speech recognition, natural language processing, structure prediction in bioinformatics and video analysis. In this tutorial, we present recent methods for solving this problem. Sequence labeling requires structured prediction which has to consider relations among predicted labels. We will first discuss linear classification methods like Fisher's linear discriminant, logistic regression and support vector machines in the binary and multi-class classification problems. We explain the relation between generative and discriminative methods for classification and draw parallels between multi-class classification and the sequence labeling problem which can be considered as a multi-class classification problem with exponentially many classes. The traditional approach of hidden Markov models for sequence labeling will be briefly covered. Hidden Markov models (HMMs)are generative models of sequential observations. HMMs have been used in sequence labeling extensively and remain as a powerful approach for many problems. The next topic of interest is conditional random fields (CRFs). CRFs have been introduced as a discriminative alternative of HMMs and are shown to outperform HMMs in some problems. The bottleneck of CRFs is that they are computationally harder to train and it may be challenging to apply for continuous observations. The tutorial will also explore how CRFs can be used for continuous observations. We will also explore the use of max-margin methods for training linear models similar to CRFs for the sequence labeling problem. Max-margin methods similar to support vector machines for multi-class classification are interesting methods for effective training of linear models for sequence labeling. This approach is also called structural SVMs and recently some algorithms for fast training of model parameters have been proposed in the literature. Some of these methods will be briefly covered in the tutorial.