Optimal Transport for Machine Learning

Related lectures (32)

Page 3 of 4

Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

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

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

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

Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

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

Covers machine learning basics, neural networks, CNNs, and model tuning.

Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

Introduces machine learning concepts, from basics to advanced neural networks.