Machine Learning Basics

Related lectures (32)

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Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

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

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

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

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

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Covers error decomposition, polynomial regression, and K Nearest-Neighbors for flexible modeling and non-linear predictions.

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

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.