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Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.
Introduces fundamental notions in digital filtering, covering 2D filtering approaches, linear filters, stability, FIR and IIR filters, frequency domain filtering, and Gaussian filters.
Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.
Covers topics in Quantum Chemistry, including history, tools, color & draw techniques, shapes, and mathematical conversions.
