Dimensionality Reduction: Rank-k Approximation and Eigen-decomposition - Lecture 20
Dive into advanced dimensionality reduction techniques, exploring rank-k approximation, eigen-decomposition, and power method applications in data analysis.
Dimensionality Reduction: Principal Component Analysis and Multidimensional Scaling - Lecture 21
Master dimensionality reduction techniques through Principal Component Analysis (PCA) and Multidimensional Scaling (MDS), exploring their applications in data analysis and visualization.
Data Mining: Frequent Directions and Random Projections - Spring 2023
Explore dimensionality reduction techniques through random projections, frequent directions, and SVD, with practical applications including the Linformer architecture for data mining.
QiML 2.0: Speed-Ups, Scalability, and Performance for New Machine Learning Era
Explore quantum-inspired machine learning advancements, including dequantized algorithms, tensor network approximations, and scalability improvements for healthcare applications and complex systems.
Tensor Networks vs. PCA and PLS for High Dimensional Medical Datasets
Explore dimensionality reduction techniques for high-dimensional medical data. Compare tensor networks with PCA and PLS, gaining insights on their applications and effectiveness in handling complex datasets.
Spectral Learning
Explore spectral learning techniques and their applications in machine learning and data analysis with Lorenzo Chicchi's comprehensive lecture.
Quantitative Approach for Measuring Student Engagement in E-learning Platforms
Discover a data-driven approach to measuring student engagement in e-learning platforms, exploring behavioral patterns, trajectory analysis, and intervention strategies for K12 math education.