Gaussian Mixture Models Courses and Certifications

UofU Data Science

Mixture of Gaussians and Mean Shift Clustering - Lecture 24

Dive into advanced clustering techniques, exploring Gaussian mixtures, EM algorithms, and Mean Shift methods for sophisticated data analysis and pattern recognition.

• YouTube
• 1 hour 21 minutes
• On-Demand
• Free Video

Cluster Analysis and Unsupervised Machine Learning in Python

5145 ratings at Udemy

Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.

• Udemy
• 7 hours 56 minutes
• On-Demand
• Paid Course

Complete Your First Project in R

27 ratings at LinkedIn Learning

This course will provide a life-like application R programmers can utilize to enhance their learning along with providing them a project they can add to their coding portfolios.

• LinkedIn Learning
• 2 hours 15 minutes
• On-Demand
• Free Trial Available

Shashank Kalanithi

Hands on Machine Learning - Unsupervised Learning

Explore unsupervised learning techniques including clustering, dimensionality reduction, and Gaussian mixture models. Hands-on implementation using Python libraries for data analysis and visualization.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Alfredo Canziani

PCA, AE, K-Means, Gaussian Mixture Model, Sparse Coding, and Intuitive VAE

Explore advanced machine learning techniques including PCA, autoencoders, K-means, Gaussian mixture models, sparse coding, and VAEs. Gain insights into their applications and intuitive interpretations.

• YouTube
• 1 hour 54 minutes
• On-Demand
• Free Video

Serrano.Academy

Unsupervised Learning

Explore key unsupervised learning techniques including clustering, dimensionality reduction, and generative models. Gain insights into real-world applications like recommendation systems and image compression.

• YouTube
• 4 hours 30 minutes
• On-Demand
• Free Video

IEEE

Small Covers for Near-zero Sets of Polynomials and Learning Latent Variable Models

Explore techniques for parameter estimation in Gaussian mixture models and mixtures of linear regressions, focusing on dimension reduction and higher moments to overcome obstacles in learning latent variable models.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Paul G. Allen School

Statistical Applications of Wasserstein Gradient Flows

Explore statistical applications of Wasserstein gradient flows in optimal transport, covering Otto calculus, Riemannian structures, and applications to variational inference and Gaussian mixture models.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Centre International de Rencontres Mathématiques

Clustering with Tangles - Machine Learning and Signal Processing on Graphs

Explore clustering with tangles, covering partitions, agreement parameters, cost functions, and applications in machine learning and signal processing on graphs.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

I-Vector Representation Based on GMM and DNN for Audio Classification - 2015

Explore I-vector approach for audio classification, including GMM mean modeling and recent subspace techniques. Learn about applications in language and dialect recognition using deep neural networks.

• YouTube
• 56 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Sequence Kernels for Speaker and Speech Recognition

Explore sequence kernels for speaker and speech recognition, focusing on generative kernels and their applications in feature extraction and classification using HMMs, GMMs, and SVMs.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

UBM-based Acoustic Modeling for ASR - 2009

Explore advanced UBM-based acoustic modeling techniques for automatic speech recognition, enhancing ASR system performance and accuracy.

• YouTube
• 1 hour 27 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

Time Independent ICA through a Fisher Game

Explore Extreme Physical Information theory and its application to Independent Component Analysis through a Fisher game framework. Learn about quantum mechanical connections and data reconstruction techniques.

• YouTube
• 1 hour 5 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

I-Vector Representation Based on GMM and DNN for Audio Classification

Explore advanced I-vector techniques for audio classification, including GMM and DNN-based approaches. Learn about subspace modeling and applications in language and dialect recognition.

• YouTube
• 57 minutes
• On-Demand
• Free Video

USC Probability and Statistics Seminar

Clustering a Mixture of Gaussians with Unknown Covariance - Lecture

Explore clustering with Gaussian mixtures, focusing on maximum likelihood estimation, Max-Cut solutions, and efficient algorithms. Investigate statistical-computational gaps and challenges in high-dimensional settings.

• YouTube
• 46 minutes
• On-Demand
• Free Video