Lectures related to Least absolute deviations

Statistical Analysis: Dispersion and Normal Values

Explores statistical dispersion and its impact on determining normal values and data analysis.

Explores the Wiener filter for signal estimation and adaptive denoising in scenarios with non-changing properties and real-time constraints.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Absolute Values: Equations and Inequalities

Explores absolute values in equations and inequalities, focusing on solving |f(x)| = g(x).

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

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

Linear Regression: Fundamentals and Applications

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

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.

Recursive Least Squares

Explains the Recursive Least Squares algorithm for updating parameter estimates in linear regression models.

Inference: Poisson Regression

Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.

Thematic Attributes and Classification

Covers statistical thematic mapping, types of maps, discretization methods, and proportional symbols in maps.

Linear Regression: Basics and Applications

Explains the least-squares regression line, correlation coefficients, outliers, influential points, and residuals in regression models.

Change of Variables Theorem

Explores the change of variables theorem and examples of polar coordinates.

Model Selection in Statistics

Explores model selection in statistics, discussing principles, probabilistic models, characteristics evaluation, and data visualization methods.

Linear Regression: Basics and Applications

Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.

Ordinary Least Squares Regression Analysis

Covers Ordinary Least Squares Regression analysis and regression model restrictions.

General Linear Model: Symmetric Matrices

Covers the general linear model and the concept of symmetric matrices.

Modern Regression: Spring Barley Data

Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.

Recursive Least-Squares: Weighted Formulation

Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.

Lagrange Multipliers: Extremum Points

Explains Lagrange multipliers for extremum points and multiple integration methods for volume and area calculations.

Large Deviations: Theory and Applications

Delves into large deviations in statistical mechanics, exploring Kramer's theorem, Varadhan's lemma, and Laplace's method.