Regression toward the mean

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Related lectures (32)

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Multi-linear regression

Covers the concept of multi-linear regression and the least squares method for model fitting.

Linear Models: Part 1

Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.

Machine Learning Basics: Supervised Learning

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.

Neural Networks: Regression and Classification

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Experimental Design and Analysis

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.

Regression: Exercises

Covers exercises on regression functions using RLS, WLS, and LWR.

Signature Kernels: Universal Feature Sets for Data Science

Explores signature kernels, feature maps, tensor algebras, and their applications in data science and machine learning.

Learning control laws with DS

Explores learning control laws with Dynamical Systems for robots, focusing on regression problems and fitting techniques.

Linear Models: Ridge, OLS and LASSO

Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.