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Related lectures (29)

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

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Back to Linear Regression

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Introduction to Machine Learning: Supervised Learning

Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Estimation, Shrinkage and Penalization

Covers estimation, shrinkage, and penalization in statistics for data science, emphasizing the importance of balancing bias and variance in model estimation.

Functional Linear Regression: Sparse Estimation and Adaptive Methods

By Angelina Roche covers adaptive and sparse estimation in functional linear regression models.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Generalized Linear Regression

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

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