Quantum Chemistry Applications

Related lectures (32)

Page 3 of 4

Explores binary classification by regression, decision functions, and various cost functions.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

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

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

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

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

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

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

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.