Optimization Problems: Gradient Descent Algorithm

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Page 3 of 4

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

Covers supervised learning through likelihood maximization to find optimal parameters.

Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.

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

Explores generalized linear regression, logistic regression, and multiclass classification in machine learning.

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Covers the basics of linear regression, including 1D regression, derivatives, gradients, and applications in machine learning.

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

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

Covers the analysis of various data attributes and linear regression models.