Classification: Introduction

Related lectures (32)

Page 2 of 4

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

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

Covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, and clustering.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

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

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Covers clustering and classification, building models to assign objects to classes based on attribute values.