This script illustrates the use of the EM Algorithm in a Gaussian mixture model
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Updated
Jul 12, 2018 - Jupyter Notebook
This script illustrates the use of the EM Algorithm in a Gaussian mixture model
Text generation with LSTM and MLC
simulate funnel analysis and study the user churn rate
Mnist dataset in specific forms (2,4,6 & 8 labels or mean brightness 2D arrays) was utilized for dimensionality reduction, clustering and classification implementations for educational purposes.
NTHU EE6550: Machine Learning
The Dirichlet Mechanism for Differentially Private KL Divergence Minimization
An introduction into the world of machine learning with a comprehensive Udemy online course, designed for beginners, to learn Python programming fundamentals and gain valuable insights into the practical applications of machine learning.
Code for the paper "Differentiable Task Graph Learning: Procedural Activity Representation and Online Mistake Detection from Egocentric Videos"
Bayesian and maximum likelihood fits
It is a jupyter notebook which examine the varience and bias parameters of maximum likelihood and maximum a posteriori approaches for biomedical imaging.
Research Operations Project by Ilham (140810160021), Alif (140810160029), and Patricia (140810160065).
Supplementary files for the paper "Environmental DNA metabarcoding of Danish soil samples reveals new insight into the hidden diversity of eutardigrades in Denmark"
Likelihood computation of a phylogenetic tree for dummies
Texas crime distribution analysis and regression modeling
Snakemake workflow that concatenate MSA files into a supermatrix and calculates a maximum likelihood tree. Imported from my GitLab
Logistic Regression is one of the basic yet complex machine learning algorithm. This is often the starting point of a classification problem. This repository will help in understanding the theory/working behind logistic regression and the code will help in implementing the same in Python. Also, This is a basic implementation of Logistic Regressi…
Formulate likelihood problems and solve them with maximum likelihood estimation (MLE)
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