Covers modeling temporal dependence in time series, including trend, periodic components, regression, stationarity, autocorrelation, and independence testing.
Explores maximum likelihood estimation in linear models, covering Gaussian noise, covariance estimation, and support vector machines for classification problems.
Explores data augmentation as a key regularization method in deep learning, covering techniques like translations, rotations, and artistic style transfer.
Explores intermodulation effects in two-tone input signals, demonstrating nonlinearity impact on signal quality and discussing different types of LNAs.
