Covers the properties of signal/data representations using Fourier transformations and matrices.
Explores signal representations, including Fourier transform, time-frequency analysis, and multi-resolution analysis using window functions like Gaussians.
Covers the representation of signals in vector spaces and inner product spaces, including the Projection Theorem.
Explores wavelets as orthonormal bases for piecewise constant signals over unit intervals.
Covers signal representations using Fourier transforms and projection theorems in the time-frequency plane.
Explores estimation, detection, linear estimators, and the orthogonality principle in machine learning.
Covers multi-arm bandits in reinforcement learning, exploring the trade-off between exploration and exploitation to minimize regret.
Discusses algorithms for balancing exploration and exploitation in decision-making processes.
