Machine Learning Basics: Supervised and Unsupervised Learning

Related lectures (43)

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

Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

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

Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Covers supervised learning through likelihood maximization to find optimal parameters.

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Covers the formalism for supervised learning and decision functions in classification problems.

Explores conditional density, expectations, and independence of random variables with practical examples.

Discusses overfitting, model selection, cross-validation, regularization, data representations, and handling imbalanced data in machine learning.

Explores soil sealing impact, land use statistics, image segmentation, and random forest classification for sustainable land management.

Explores thresholding, ROC curves, regression evaluation, naive methods, and model selection in machine learning.

Page 2 of 3