Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Covers linear problems, LASSO, and AMP in supervised learning, including Generalized Linear Models and N-dimensional models.
Explores electrical metrology, covering random variables, noise sources, and their impact on electronic devices.
Explores electronic, thermomechanical, and amplifier noise, calibration of amplitude, frequency tracking, and system limits.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.
