Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.
Covers ARMA models for time series forecasting, discussing implications, properties of forecast error, challenges with predictions, and covariance models.
Covers model selection, diagnostics, and forecasting in time series analysis, emphasizing the challenges of determining the model order based on autocorrelation and partial autocorrelation functions.
Explores demand forecasting through model initiation, including trend identification, seasonal components, and base level determination, to validate and monitor forecast errors.
Explores safe automation challenges for intelligent systems, focusing on self-driving cars and proposing solutions based on system dynamics and filters.
