Automated Chemical Synthesis: Catalyst Design & Optimization

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (30)

Page 1 of 3

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Machine learning: Physics and Data

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Graphs in Deep Learning: Applications and Techniques

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Untitled

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Machine Learning: Supervised and Unsupervised Learning Techniques

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

Introduction to Machine Learning: Regression Fundamentals

Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.

Linear Regression: Foundations and Applications

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

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.