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.
Data-Driven Decision Making with Entropic Risk Measure
Explore data-driven decision-making using entropic risk measure. Learn bias correction techniques and apply them to project selection, portfolio optimization, and insurance pricing.
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.
Stochastic Dynamics of the Microbiome
Explore stochastic dynamics of microbiomes through advanced computational methods. Gain insights into microbial growth, spatial structure, and population trajectories using cutting-edge genomic analysis techniques.
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.
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.
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.
Expression Analysis, Clustering, and Classification in Machine Learning - Lecture 2
Comprehensive exploration of machine learning concepts, from basic principles to advanced techniques, with applications in gene expression analysis and text clustering.
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.
Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models
Delve into high-dimensional mean estimation in binary Markov Gaussian mixture models, exploring how memory in data affects estimation error and the interplay between sample size, dimension, and signal strength.
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.
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.
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.
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.
UBM-based Acoustic Modeling for ASR - 2009
Explore advanced UBM-based acoustic modeling techniques for automatic speech recognition, enhancing ASR system performance and accuracy.
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.