Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Covers the importance of preventive maintenance for pavement distress detection and introduces machine learning concepts for engineers.
Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.
Explores supervised learning as an ill-posed problem and the integration of sparse adaptive splines into neural architectures.
