Lectures related to Total least squares

Hadamard Design: Introduction and Construction

Introduces Hadamard and Plackett-Burman designs, explaining construction, analysis, and optimization techniques.

Introduces the Hubble law, parsec, and redshift in observational cosmology.

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.

Multilinear Regression: Least Square Fit

Covers multilinear regression using least square fit method and the importance of standardizing variables.

Modeling Response Surfaces in Matlab

Covers modeling response surfaces in Matlab, including building the model, defining the domain, computing values, and analyzing residuals.

Regression: Linear Models

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Modeling Elasticity and Coefficients

Explores correlation matrices, regression, variance, confidence intervals, and standardized systems in statistical modeling.

Newton's method: Hessian and optimization

Explores Newton's method for optimization, emphasizing the role of the Hessian matrix.

Least-Squares Approximation

Covers polynomial interpolation and least-squares approximations to minimize errors.

Signal Approximation and Orthogonal Bases

Explores signal approximation, orthogonal bases, Fourier series, correlation, and vector geometry.

Plackett-Burman Design: Hadamard Matrix

Explores the construction and application of Hadamard matrices for efficient estimation of main effects without interactions in the Plackett-Burman Design.

Statistical Inference for Bandit Data

Explores statistical inference for bandit data, focusing on personalized treatment actions and challenges of standard estimators.

Multilinear Regression: Basics and Applications

Covers the basics of multilinear regression and its application in analyzing material properties.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Linear Equations: Least Squares Solution

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Distributed Computation: Consensus Algorithms in Networked Control Systems

Explores consensus algorithms in networked control systems, covering topics like Metropolis-Hasting models and distributed computation of Least-Squares regression.

Parameter Estimation of SDEs with Linear Response Theory

Explores parameter estimation of SDEs using Linear Response Theory and covers challenges, examples, algorithms, and convergence.

Splines: Fundamentals and Applications

Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.

Least Squares Approximation

Explains least squares approximation for finding best fit lines or curves to data points.

Examples of decision models for Supervised learning

Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.