Machine Learning for Behavioral Data

Related lectures (31)

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Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

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

Outlines the project for building educational chatbots for EPFL courses, detailing its structure and requirements.

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

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

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

Covers machine learning approaches for material design, practical examples, and software tools for research.

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Covers the analysis of air pollution data, focusing on R basics, visualizing time series, and creating summaries of pollutant concentrations.