Machine Learning in Molecular Dynamics

Related lectures (31)

Page 3 of 4

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

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

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

Covers nonparametric regression using kernel-based estimation techniques to model complex relationships between variables.

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Covers supervised learning, classification, regression, decision boundaries, overfitting, Perceptron, SVM, and logistic regression.

Covers the transition from linear to nonlinear models, focusing on k-NN and feature expansion techniques.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.