Concept
Mean percentage error
Related lectures (11)
Mean-Square-Error Inference
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
Linear Regression Basics
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Regression Models: Performance and Evaluation
Explores regression model performance, learning errors, and building regression trees using the CART algorithm.
Binary Representation of Real NumbersMOOC: Information, Calcul, Communication: Introduction à la pensée informatique
Discusses the binary representation of real numbers and the precision errors involved.
Optimization: Gradient Descent and Subgradients
Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Demand Forecasting: Model Initiation
Explores demand forecasting through model initiation, including trend identification, seasonal components, and base level determination, to validate and monitor forecast errors.
Operation of Distributed Energy Storage Systems
By the instructor Mario Paolone explores the challenges and solutions of integrating distributed energy storage systems into power grids.
Loss Functions and Optimization
Explores loss functions, gradient descent, and step size impact on optimization in machine learning models, highlighting the delicate balance required for efficient convergence.
Demand Forecasting Methods
Explores demand forecasting steps and the Holt-Winter model for time series analysis.