Mathematics of Data: Models and Estimators

Related lectures (30)

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Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

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

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

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

Covers nonparametric regression, scatterplot smoothing, kernel methods, and bias-variance tradeoff.

Delves into statistical estimation, highlighting the Gaussian linear model and the limitations of ML estimators.

Introduces linear models in machine learning, covering basics, parametric models, multi-output regression, and evaluation metrics.

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