course by Daphne Koller - Stanford University
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Updated
Mar 22, 2017 - MATLAB
course by Daphne Koller - Stanford University
These are my solutions to the assignments of the probabilistic graphical models class offered by coursera
The homework assignments finished for the coursera specialization "Probabilistic Graphical Models"
This is where I play and learn about machine learning applications.
Nonnegative matrix factorization with DAG constraints. A probabilistic formulation, variational learning.
Offline Simultaneous Localization and Mapping using GTSAM
A new take on EEG sleep spindles detection exploiting a generative model (dynamic bayesian network) to characterize reoccurring dynamical regimes of single-channel EEG.
Designing and training probabilistic graphical models (MATLAB).
Identifiability of AMP chain graph
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.
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