Regression: High Dimensions

Related lectures (31)

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Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

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

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

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

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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

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

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