Supervised Learning Overview

Related lectures (32)

Page 3 of 4

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

Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.

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

Covers clustering, semi-supervised clustering, and binary classification formalization, along with various classification techniques.

Covers key concepts and examples of machine learning algorithms and techniques.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Explores efficient machine learning through data summarization, covering challenges, methods, and impactful applications in various domains.

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.