The homework assignments finished for the coursera specialization "Probabilistic Graphical Models"
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Updated
Jun 16, 2017 - MATLAB
The homework assignments finished for the coursera specialization "Probabilistic Graphical Models"
course by Daphne Koller - Stanford University
Offline Simultaneous Localization and Mapping using GTSAM
These are my solutions to the assignments of the probabilistic graphical models class offered by coursera
A new take on EEG sleep spindles detection exploiting a generative model (dynamic bayesian network) to characterize reoccurring dynamical regimes of single-channel EEG.
Implementation of various inference and learning algorithms for Probabilistic Graphical Models (PGMs) without off-the-shelf libraries. Also includes projects from the PGM specialization on Coursera offered by Stanford.
Identifiability of AMP chain graph
Designing and training probabilistic graphical models (MATLAB).
Nonnegative matrix factorization with DAG constraints. A probabilistic formulation, variational learning.
This is where I play and learn about machine learning applications.
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