Dimensionality Reduction for Matrix- and Tensor-Coded Data
Explore matrix and tensor factorizations for data compression, including PCA, NMF, and CP tensor decomposition. Learn theoretical foundations and practical applications in scientific domains.
Dimensionality Reduction II
Explore advanced dimensionality reduction techniques, including PCA and negentropy, with practical applications in brain and cognitive sciences. Includes exercises and research discussions.
Autoencoders
Explore autoencoders, from basic concepts to advanced applications, covering compression, PCA, deep architectures, sparse representations, and denoising techniques.
Unsupervised Feature Learning with Matrix Decomposition - Aedin Culhane, PhD | ODSC East 2018
Explore unsupervised learning techniques for high-dimensional data analysis, focusing on matrix factorization methods and their applications in oncology and precision medicine.
Katharine Turner - PCA of Persistent Homology Rank Functions with Case Studies
Explore persistent homology rank functions through PCA, featuring case studies and insights into topological data analysis techniques.
On Bayesian Models with Networks for Reconstruction and Detection
Explore Bayesian models with neural networks for MRI reconstruction and outlier detection in medical imaging, focusing on network-based priors and generative modeling approaches.
Wobbling, Drooping, Bouncing - Visual Perception of Materials and Their Properties
Explore visual perception of material properties through experiments on elastic bouncing objects, examining human estimation accuracy, motion features, and internal models for predicting object behavior.
Practical Approach for Fingerprint Recognition by Merging LBP and PCA
Explore fingerprint recognition using LBP and PCA techniques for improved accuracy. Dr. Solanki demonstrates practical implementation, enhancing computer science applications in forensic analysis.
Randomization - Speeding Up Huge Problems
Explore data reduction techniques for analyzing large datasets efficiently, focusing on randomized sub-sampling and local-rank approximations in PCA and sparse-PCA contexts.
Deep Learning Applied to the Analysis of Dissolved Carbon Dioxide in Coastal Areas of the Balearic Islands
Explore deep learning techniques for analyzing dissolved CO2 in Balearic coastal areas, covering self-attention, permutation convolution, and symbolic regression.
Machine Learning Toolbox for Cybersecurity Risk Management
Explore machine learning techniques for cybersecurity risk assessment, including topic modeling, word embedding, and PCA. Learn to identify unknown risks and profile incidents effectively.
Residual Based Sampling for Online Low Rank Approximation
Explore online low rank approximation through residual-based sampling, covering PCA, column subset selection, and online algorithms. Learn techniques for efficient data analysis and clustering.
Fast Analytics On Big Data
Explore fast analytics on big data using an open-source platform for in-memory distributed data processing, enabling efficient predictive modeling and analytics on large-scale datasets.
Sum-of-Minimum Model: Joint Optimization of Specialized Models for Heterogeneous Data
Explore joint optimization of specialized models for heterogeneous data using the sum-of-minimum approach. Learn algorithm design, performance bounds, and applications in various machine learning tasks.
Prometheus Certified Associate (PCA) Certification Overview and Demo
Explore Prometheus certification, prepare with resources, and learn to instrument applications for data pushing to Prometheus via pushgateway. Includes demo and comprehensive course overview.
Variational Inference for Fun and Profit
Discover the fundamentals of variational inference in statistical analysis, exploring Bayesian techniques, SuSiE PCA methodology, and practical applications in computational genomics and regression modeling.
Dimensionality Reduction: Data Matrices and Projection Methods - Lecture 18
Explore dimensionality reduction techniques through matrix operations, focusing on dot product projections and orthogonal basis sets for effective data transformation.
Random Projection in Data Mining - Spring 2023
Explore random projection techniques in data mining, from PCA and SVD fundamentals to Johnson-Lindenstrauss lemma, with practical algorithms and theoretical foundations.
Building Decision-Making Assistants: Introduction to Recommendation Systems
Explore recommendation systems from basic classification to advanced Netflix-style matrix factorization, covering content filtering, collaboration models, SVD, and PCA techniques.
An ℓp Theory of PCA and Spectral Clustering
Explore an advanced $\ell_p$ theory of PCA, improving upon vanilla PCA for heteroscedastic noises and yielding optimal bounds for spectral clustering and community detection.
Bayesian Limits in Structured PCA and How to Reach Them - Workshop on Spin Glasses
Explore Bayesian limits in structured PCA and techniques to achieve them, presented by J. Barbier from ICTP Trieste.