Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
PCA: Derivation and Optimization
Graph Chatbot
Related lectures (53)
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Oja's Rule
Covers Oja's rule in Neurorobotics, focusing on learning eigenvectors and eigenvalues for capturing maximal variance.
Multivariate Statistics: Wishart and Hotelling T²
Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.
Non-Negative Definite Matrices and Covariance Matrices
Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.
Market Response Functions
Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.
Estimating the Term Structure: Principal Component Analysis
Covers Principal Component Analysis for yield curve shape estimation and dimension reduction in interest rate models.
Building Robust Ensembles via Margin Boosting
Delves into building robust ensembles through margin boosting for improved adversarial defense in machine learning models.
Principal Component Analysis: Theory and Applications
Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.
Principal Component Analysis: Dimension Reduction
Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.
Optimization Methods: Theory Discussion
Explores optimization methods, including unconstrained problems, linear programming, and heuristic approaches.
Dependence in Random Vectors
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Coin Rendering: Part 1
MOOC: Information, Computation, Communication: Introduction to computational thinking
Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.
Covariance Cleaning and Estimators
Explores covariance matrix cleaning, optimal estimators, and rotationally invariant methods for portfolio optimization.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Linear Dimensionality Reduction: PCA and LDA
Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.
Multivariate Statistics: Introduction and Methods
Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.
Unsupervised Learning: PCA & K-means
Log in to Mediaspace to watch this video
Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Principal Component Analysis: Olympic Medals & Image Compression
Log in to Mediaspace to watch this video
Explores PCA for predicting medals distribution and compressing face images.
Dimensionality Reduction: PCA & LDA
Log in to Mediaspace to watch this video
Covers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Simplex Algorithm: Basics
Log in to Mediaspace to watch this video
Introduces the Simplex algorithm for solving flow problems and handling negative cost cycles.
Previous
Page 1 of 3
Next