Hidden Markov Models in a Nutshell

We're excited to have Oualid Missaoui at the podium to share his extensive experience with Hidden Markov Models. His talk description and bio follow...

Description: Hidden Markov Models (HMMs) have emerged as a powerful paradigm for modeling stochastic processes and pattern sequences. Originally, HMMs have been applied to the domain of speech recognition, and became the dominating technology. In recent years, they have attracted growing interest in automatic target detection and classification, computational molecular biology, bioinformatics, computational finance, mine detection, handwritten character/word recognition, and other computer vision applications. The purpose of this talk is to define HMM and its categories, present the corresponding underlying problems, and explain the step-by-step working of the most popular procedure for HMM parameter estimation: Baum-Welch algorithm.

Bio: Oualid Missaoui is researcher with Pipeline Financial Group, Inc. where he is in charge of developing data mining and pattern recognition based algorithmic trading framework. He received his Ph.D. in Computer Engineering & Science for his research in the fields of machine learning, landmine detection, and image processing, from University of Louisville (2010). He earned his engineering degree in Signal and Systems and M.Sc. in Applied Mathematics from Ecole Polytechnique de Tunisie (2003, 2005).