Machine learning, in numpy

Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences

Framework to learn Named Entity Recognition models without labelled data using weak supervision.

Bayesian hidden Markov models toolkit

Hidden Markov Models for Julia.

An unsupervised machine learning algorithm for the segmentation of spatial data sets.

Full Bayesian Inference for Hidden Markov Models

Python toolkit for Visual Speech Recognition

Markov Chains and Hidden Markov Models to generate and correct sentences

💻 A repository with comprehensive instructions for using the Festvox toolkit for generating Emotional speech 🔈 from text

Hidden Markov Models - Viterbi and Baum-Welch algorithm implementation in Python

SigMa is a probabilistic model for the sequential dependencies of mutation signatures

Fast Bayesian Hidden Markov Model with Wavelet Compression

Hidden Markov Models in JavaScript.

Term 1 Project 3 Design a Sign Language Recognition System by Luke Schoen for Udacity Artificial Intelligence Nanodegree (AIND)

Hidden Markov models for word representations

Detection of events described by hidden (semi-)Markov models in an audio stream

python pYIN

This repo consists of the projects that I completed as a part of the Udacity's Artificial Intelligence Nanodegree's curriculum.

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Artificial Intelligence Nanodregree from Udacity

Compilation of Natural Language Processing (NLP) codes. BONUS: Link to Information Retrieval (IR) codes compilation. (checkout the readme)

A FDR controlling procedure based on hidden Markov random field (Biometrics-15 paper)

A machine learning interface for isolated temporal sequence classification algorithms in Python.

Variational Hierarchical EM for HMM clustering.

Java Utility for Class Hidden Markov Models and Extensions

Probabilistic graphical models home works (MVA - ENS Cachan)

The goal of this script is to implement three langauge models to perform sentence completion, i.e. given a sentence with a missing word to choose the correct one from a list of candidate words. The way to use a language model for this problem is to consider a possible candidate word for the sentence at a time and then ask the language model which version of the sentence is the most probable one.

Rendition of a well known techniques: forward/backward, Baum-Welch, and Viterbi algorithms. Tested on a simulated random Hidden Markov processes.