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Related lectures (32)

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Kernel Methods: Machine Learning

Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.

Basics of Mobile Robotics: Uncertainties

Explores the fundamentals of mobile robotics, emphasizing uncertainties in localization, sensor fusion, and the Extended Kalman Filter.

Gaussian Distribution Basics

Covers the basics of Gaussian distribution, including linear transformations and stability theorems.

Digital Filtering Fundamentals

Introduces fundamental notions in digital filtering, covering 2D filtering approaches, linear filters, stability, FIR and IIR filters, frequency domain filtering, and Gaussian filters.

Neural Networks and Quantum Field Theory

Explores the connection between neural networks and quantum field theory, focusing on the correspondence between parameter and function spaces.

Plancherel's Identity and Schrödinger Equation

Covers Plancherel's identity and the solution of the free Schrödinger equation with initial data in Schwartz space.

Cooperation in Kohonen Maps

Explores the concept of cooperation in Kohonen maps using a Gaussian function.

Laplace Approximation: Integral Estimation

Explores Laplace approximation for integral estimation using smooth convex functions and Gaussian distribution.

Image Processing Techniques

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Beam Optics: Introduction and Applications

Covers beam optics, Gaussian beams, and wavefront curvature in various applications.