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Learning From Data

Circuits and Systems

Ali Hajimiri

This course offers a holistic view of the fundamentals of circuits and system theories. It starts with fundamental physics, circuit elements, linear and nonlinear circuits, nodal and mesh analysis, network theorems, time-domain analysis of circuits and systems. It applies the impulse response and convolution in time domain to system analysis, transitioning into Heaviside operators to solve the associated differential equations. The operator theory naturally leads to the concept of complex frequency, transfer function, poles and zeros, and Laplace and Fourier domain analysis. The course develops a parallel dual time and frequency domain view of systems mostly in the concept of electrical circuits.

Learning From Data

Machine Learning

Yaser S. Abu-Mostafa

This is an introductory course on machine learning that can be taken at your own pace. It covers the basic theory, algorithms and applications. Machine learning (Scientific American introduction) is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Machine learning is one of the hottest fields of study today, taken up by graduate and undergraduate students from 15 different majors at Caltech.