Why Standard ML is Not Sufficient: Learning and Adaptive Control

Related lectures (32)

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Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.

Covers linear regression, weighted regression, locally weighted regression, support vector regression, noise handling, and eye mapping using SVR.

Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

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

Covers Support Vector Regression principles, optimization, and hyperparameters' influence on the fit.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.