Hidden Markov Models Fundamentals

Abstract How can we apply machine learning to data that is represented as a sequence of observations over time? For instance, we might be interested in discovering the sequence of words that someone spoke based on an audio recording of their speech. Or we might be interested in annotating a sequence of words with their part-of-speech tags. These notes provides a thorough mathematical introduction to the concept of Markov Models a formalism for reasoning about states over time and Hidden Markov Models where we wish to recover a series of states from a series of observations. The nal section includes some pointers to resources that present this material from other perspectives