Python implementation of EM algorithm for GMM. And visualization for 2D case.

Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza

MS Yang, A robust EM clustering algorithm for Gaussian mixture models, Pattern Recognit., 45 (2012), pp. 3950-3961

This repository is for sharing the scripts of EM algorithm and variational bayes.

Gaussian Mixture Model for Clustering

Model-based clustering based on parameterized finite Gaussian mixture models. Models are estimated by EM algorithm initialized by hierarchical model-based agglomerative clustering. The optimal model is then selected according to BIC.

ModelGaussian_Mixture_Model

Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

Clustering methods in Machine Learning includes both theory and python code of each algorithm. Algorithms include K Mean, K Mode, Hierarchical, DB Scan and Gaussian Mixture Model GMM. Interview questions on clustering are also added in the end.

A recommender system based on data provided by MHRD on colleges and universities in India. Website-

2019～2020学年第2学期《并行程序设计》课程设计

Code for the paper Data-efficient model learning and prediction for contact-rich manipulation tasks, RA-L, 2020

Gaussian Latent Dirichlet Allocation

Implementation of Task-Parameterized-Gaussian-Mixture-Models as presented from S. Calinon in his paper: "A Tutorial on Task-Parameterized Movement Learning and Retrieval"

A Python implementation of Gaussian Mixture Model (GMM)

RL and DMP algorithms implemented from scratch with plain Numpy.

Analyzing a dataset containing data on various customers' annual spending amounts of diverse product categories for internal structure. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.

Ozone profile clustering code for UKESM1

We are given 2 different problems to solve. 1. Isolated spoken digit recognition 2. Telugu Handwritten character recognition Both these datasets were given as a time series. 2 different methods were used to solve each of the problem: 1. Dynamic Time Warping 2. Hidden Markov Models