Hidden Markov Model

Hidden Markov models attempt to capture hidden sequential information that can be found in data sequences, and belong to the area of unsupervised machine learning. They have numerous applications to clustering, collaborative filtering, recommender systems, computational biology and sequence analysis, genomics, sentiment analysis, natural language processing (NLP), speech and pattern recognition, face recognition, emotion recognition, seismology, climate change studies, finance, etc.