The 3rd and final problem in Hidden Markov Model is the Decoding Problem. In this article we will implement Viterbi Algorithm in Hidden Markov Model using Python and R. Viterbi Algorithm is dynamic programming and computationally very efficient. We will start with the formal definition of the Decoding Problem, then go through the solution and finally implement it. This is the 4th part of the Introduction to Hidden Markov Model tutorial series. This one might be the easier one to follow along.
The most important and complex part of Hidden Markov Model is the
Learning Problem. Even though it can be used as Unsupervised way, the more common approach is to use Supervised learning just for defining number of hidden states. In this Derivation and implementation of Baum Welch Algorithm for Hidden Markov Model article we will go through step by step derivation process of the
Baum Welch Algorithm (a.k.a
Forward-Backward Algorithm) and then implement is using both Python and R.
Introduction to Hidden Markov Model article provided basic understanding of the Hidden Markov Model. We also went through the introduction of the three main problems of HMM (
Decoding). In this Understanding Forward and Backward Algorithm in Hidden Markov Model article we will dive deep into the Evaluation Problem. We will go through the mathematical understanding & then will use Python and R to build the algorithms by ourself.
Hidden Markov Model is an Unsupervised* Machine Learning Algorithm which is part of the Graphical Models. However Hidden Markov Model (HMM) often trained using supervised learning method in case training data is available. In this introduction to Hidden Markov Model we will learn about the foundational concept, usability, intuition of the algorithmic part and some basic examples. Only little bit of knowledge on probability will be sufficient for anyone to understand this article fully.
Coordinate Descent is another type of optimization algorithm used mainly for ‘strongly convex’ and Lasso Regression function. You are probably aware of Gradient Descent, for solving Least Square Regression. In this Introduction to Coordinate Descent using Least Squares Regression tutorial we will learn more about Coordinate Descent and then use this to solve Least Square Regression.