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 for Matrix- and Tensor-Coded Data
Explore advanced dimensionality reduction techniques for matrix and tensor data, focusing on CP tensor decomposition. Learn theoretical foundations and practical applications in neuroscience.
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.
Approximate Cross Validation for Large Data and High Dimensions - Tamara Broderick, MIT
Explore efficient cross-validation techniques for large datasets using linear approximation and dimensionality reduction. Learn about error bounds, high-dimensional challenges, and practical applications in machine learning.
Big Data Is Low Rank - Madeleine Udell, Cornell University
Explore big data's low-rank structure, generalized low-rank models, and their applications in machine learning. Learn about dimensionality reduction, autoML, and latent variable models for efficient data analysis.
Sampling-Based Sublinear Low-Rank Matrix Arithmetic Framework for Dequantizing Quantum Machine Learning
Explore quantum-inspired classical algorithms for machine learning, focusing on sublinear low-rank matrix arithmetic and singular value transformation techniques.
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.
Quantum Machine Learning- Prospects and Challenges
Explore prospects and challenges in quantum machine learning, covering classification techniques, dimensionality reduction, data loading, and benchmarking for both optimistic and cautious perspectives.
Dimensionality Reduction via Distributed Persistence - DIPOLE
Explore dimensionality reduction with DIPOLE, a novel approach combining local metric and global topological preservation for improved data visualization and analysis.
Introduction to Machine Learning - Lecture 2
Explore key machine learning concepts and their applications in healthcare, bridging the gap between computational modeling and clinical problem-solving.
Unsupervised ML - Clustering, Dimensionality Reduction, Applications by Rahul Siddharthan
Explore unsupervised machine learning techniques for clustering and dimensionality reduction, with practical applications in data analysis and pattern discovery.
OpenAI's New Embedding Models: Performance and Usage - Text-Embedding-3-Small and Text-Embedding-3-Large
Explore OpenAI's new embedding models, their performance gains, dimensionality options, and practical applications in semantic search and AI development.
Coresets and Their Applications to Design Sublinear Algorithms
Explore coresets and their role in designing efficient sublinear algorithms, enhancing computational performance for large-scale data analysis and machine learning tasks.
Img2Vec and UMAP for High-Dimensional Data Visualization
Learn to visualize high-dimensional image data using Img2Vec for embeddings, UMAP for dimensionality reduction, and Bokeh for interactive exploration. Ideal for vision classification projects.
Identifiable Deep Generative Models via Sparse Decoding
Explore sparse VAE for unsupervised learning, uncovering identifiable latent factors in high-dimensional data. Discover theoretical guarantees and practical applications in various domains.
Applications of Sub-linear Algorithms in Other Fields
Explore how sub-linear algorithms impact diverse fields, enhancing efficiency and scalability in data analysis and problem-solving across disciplines.
Point Location and Active Learning - Learning Halfspaces Almost Optimally
Explore advanced techniques in machine learning, focusing on point location, active learning, and halfspaces. Gain insights into cutting-edge algorithms and strategies for optimal learning outcomes.
CloudCluster - Unearthing the Functional Structure of a Cloud Service
Unearths cloud service functional structure using VM-to-VM traffic matrix clustering. Aids management by revealing design patterns, enabling cost reduction, and identifying anomalies and misconfigurations.
Metric Representations - Algorithms and Geometry
Explore metric representations, algorithms, and geometry in this lecture covering multidimensional scaling, metric repair, optimization techniques, and dimensionality reduction.
Visualizing Cloud Systems
Explore effective visualization techniques for cloud-native systems, focusing on microservices and serverless patterns. Learn to design and implement solutions using visual grammars and dimensionality reduction.
Machine Learning for a Rescue
Explore machine learning applications in software development, focusing on data-driven approaches to classify developers and predict project outcomes.