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Related lectures (44)
Untitled
Linear Models and Overfitting
Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.
Linear Models: Ridge, OLS and LASSO
Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.
Linear Regression Testing
Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.
Linear Models: Least Squares
Explores linear models, least squares, Gaussian vectors, and model selection methods.
Singular Value Decomposition: Theory and Applications
Explores Singular Value Decomposition theory, linear system solutions, least squares, and data fitting concepts.
Regularization: Tikhonov and Ridge
Explores Tikhonov regularization, also known as Ridge regression, and its application to polynomial regression.
Explicit Stabilised Methods: Applications to Bayesian Inverse Problems
Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.
Singular Value Decomposition: Applications and Interpretation
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Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.
Linear Regression and Logistic Regression
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Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Linear Algebra Basics
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Covers fundamental concepts in linear algebra, including linear equations, matrix operations, determinants, and vector spaces.
Convex Optimization: Notation and Matrix Norms
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Introduces Convex Optimization notation, convex functions, vector norms, and matrix properties.
Least Squares Solutions
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Covers least squares solutions for linear systems using matrix operations and normal systems, illustrated with examples.
Least Squares Solutions
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Explains the concept of least squares solutions and their application in finding the closest solution to a system of equations.
Linear Algebra: Normal Equations and Symmetric Matrices
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Explores normal equations, pseudo-solutions, unique solutions, and symmetric matrices in linear algebra.
Matrix Factorization: Least Squares Method
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Covers the factorization of a matrix and the least squares method.
Linear Algebra Review: Convex Optimization
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Covers essential linear algebra concepts for convex optimization, including vector norms, eigenvalue decomposition, and matrix properties.
Linear Regression: Basics and Estimation
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Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Linear Models: Continued
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Explores linear models, regression, multi-output prediction, classification, non-linearity, and gradient-based optimization.
Matrix Equivalence Theorems
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Explores matrix equivalence theorems for systems of equations and least squares solutions.
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