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      <title>Obsidian + 🪴 Quartz  4.0</title>
      <link>https://kb.tech.m0ka.it</link>
      <description>Last 10 notes on Obsidian + 🪴 Quartz  4.0</description>
      <generator>Quartz -- quartz.jzhao.xyz</generator>
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    <title>Scientific Computing</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/Scientific-Computing</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/Scientific-Computing</guid>
    <description><![CDATA[  Index - Scientific Computing Part Materiale di studio 📖 Cleve Moler “Calcolo Numerico con Matlab” 📖 Linear Algebra and Learning from Data Gilbert Strang Wellesley-Cambridge Press 2019 Slide del Prof su E-Learning Indice Capitoli SC - Lezione 1 - Program topics of this course, Matlab Basics, Basic... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 6 - Orthogonal projector, QR factorization, Reduced QR factorization, QR factorization for solvin Ax=b and for Least Square, Pseudoinverse of a matrix with QR</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-6---Orthogonal-projector,-QR-factorization,-Reduced-QR-factorization,-QR-factorization-for-solvin-Ax=b-and-for-Least-Square,-Pseudoinverse-of-a-matrix-with-QR</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-6---Orthogonal-projector,-QR-factorization,-Reduced-QR-factorization,-QR-factorization-for-solvin-Ax=b-and-for-Least-Square,-Pseudoinverse-of-a-matrix-with-QR</guid>
    <description><![CDATA[  Checklist Checklist Domande, Keyword e Vocabulary Orthogonal projector Span QR factorization of a matrix Reduced QR factorization Trick to compute a reduced rank matrix Basis Orthogonal complement to the range of A Perpendicular vector QR Factorization for solving Expensiveness Normal Equations QR ... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 7 - Deriving the least square formula as orthogonal projection, orthogonal basis for the range of A, QR Applications, Latent Semantic Analysis, Overdetermined System</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-7---Deriving-the-least-square-formula-as-orthogonal-projection,-orthogonal-basis-for-the-range-of-A,-QR-Applications,-Latent-Semantic-Analysis,-Overdetermined-System</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-7---Deriving-the-least-square-formula-as-orthogonal-projection,-orthogonal-basis-for-the-range-of-A,-QR-Applications,-Latent-Semantic-Analysis,-Overdetermined-System</guid>
    <description><![CDATA[  SC - Lezione 7 Checklist Checklist Domande, Keyword e Vocabulary What’s the other way to derive the formula as orthogonal projection b on the range of A? What is an orthogonal basis for the range of A? Range of a matrix Latent Semantic Analysis Documents and keyword example Query as vector Semantic... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 8 - Eigenvalue, Eigenvectors, Spectral Decomposition, QR algorithm and Power Method</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-8---Eigenvalue,-Eigenvectors,-Spectral-Decomposition,-QR-algorithm-and-Power-Method</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-8---Eigenvalue,-Eigenvectors,-Spectral-Decomposition,-QR-algorithm-and-Power-Method</guid>
    <description><![CDATA[  SC - Lezione 8 - Eigenvalue, Eigenvectors, Spectral Decomposition, QR algorithm and Power Method Checklist Checklist Domande, Keyword e Vocabulary Eigenvalue Eigenvector Eigenvalues, Eigenvectors and complex numbers Physical meaning of an eigenvector Complex eigenvalues and eigenvector Polynomial C... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
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    <title>SC - Lezione 9 - Singular Value Decomposition</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-9---Singular-Value-Decomposition</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-9---Singular-Value-Decomposition</guid>
    <description><![CDATA[  SC - Lezione 9 Checklist Checklist Domande, Keyword e Vocabulary Singular Value Decomposition Two orthogonal and a diagonal Singular Values Left and right singular vectors What happens if you multiply a vector by a diagonal matrix? Directions of sigma1 and sigma2 Semiaxis sigma1 and sigma2 Economy ... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 3 - Transposition of matrix-vector equation, Vector and Matrix Norm, Unit norm vector, Frobenius Norm, Angle between vectors, Orthogonal Projections and Sparse Matrices</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-3---Transposition-of-matrix-vector-equation,-Vector-and-Matrix-Norm,-Unit-norm-vector,-Frobenius-Norm,-Angle-between-vectors,-Orthogonal-Projections-and-Sparse-Matrices</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-3---Transposition-of-matrix-vector-equation,-Vector-and-Matrix-Norm,-Unit-norm-vector,-Frobenius-Norm,-Angle-between-vectors,-Orthogonal-Projections-and-Sparse-Matrices</guid>
    <description><![CDATA[  SC - Lezione 3 Checklist Domande, Keyword e Vocabulary Transposed product equation Vector and matrix norm norm in matlab Unit-norm vector Frobenius Norm Stack representation of a matrix Angle between vectors Orthogonal Projections Esempio vettore z parole Sparse Matrices Appunti SC - Lezione 3 Tran... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 30 - 2D CWT, Discrete Wavelet Transform and its inverse, Fast Wavelet Transform Algorithm</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-30---2D-CWT,-Discrete-Wavelet-Transform-and-its-inverse,-Fast-Wavelet-Transform-Algorithm</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-30---2D-CWT,-Discrete-Wavelet-Transform-and-its-inverse,-Fast-Wavelet-Transform-Algorithm</guid>
    <description><![CDATA[  SC - Lezione 30 Checklist Checklist Domande, Keyword e Vocabulary 2-D Continuos Wavelet Transform and its inverse Comparison with the 1D Wavelet Transform Two translation parameters Orientation Parameter 4D Tensor Algorithm for Discrete 2D Wavelet Transform Exercise: facial feature extraction with ... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 31 - 2D DWT and its inverse, JPEG2000, High-pass and Low-pass filters in the Time domain</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-31---2D-DWT-and-its-inverse,-JPEG2000,-High-pass-and-Low-pass-filters-in-the-Time-domain</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-31---2D-DWT-and-its-inverse,-JPEG2000,-High-pass-and-Low-pass-filters-in-the-Time-domain</guid>
    <description><![CDATA[  SC - Lezione 31 Checklist Checklist Domande, Keyword e Vocabulary DWT Application: denoising a signal Median Absolute Deviation Universal Threshold 2D-DWT and its inverse Matrix G rows and columns Single Level 2D-DWT steps LL, HL, LH, HH matrices approximation, horizontal, vertical, diagonal detail... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 4 - Projection Matrix, Recall of applications of scientific computing, Variance and Average, Product of block matrices, Orthogonal matrices, square matrices, matrix norm, rotation matrix</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-4---Projection-Matrix,-Recall-of-applications-of-scientific-computing,-Variance-and-Average,-Product-of-block-matrices,-Orthogonal-matrices,-square-matrices,-matrix-norm,-rotation-matrix</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-4---Projection-Matrix,-Recall-of-applications-of-scientific-computing,-Variance-and-Average,-Product-of-block-matrices,-Orthogonal-matrices,-square-matrices,-matrix-norm,-rotation-matrix</guid>
    <description><![CDATA[  SC - Lezione 4 Domande, Keyword e Vocabulary Projection Matrix Least Square Fitting Product of block matrices Square Matrix Linear Transformation 2-norm = 1 Mutually orthogonality Inverse Matrix of orthogonal matrix Rotation Matrix Appunti SC - Lezione 4 Projection Matrix In the formula we have: at... ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
  </item><item>
    <title>SC - Lezione 5 - Laboratory - Properties of orthogonal matrix and orthogonal transformations</title>
    <link>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-5---Laboratory---Properties-of-orthogonal-matrix-and-orthogonal-transformations</link>
    <guid>https://kb.tech.m0ka.it/University-Exams/Bachelor-Degree-in-ML-and-Big-Data/Scientific-Computing/SC---Lezione-5---Laboratory---Properties-of-orthogonal-matrix-and-orthogonal-transformations</guid>
    <description><![CDATA[  SC - Lezione 5 Checklist Domande, Keyword e Vocabulary Appunti SC - Lezione 5 - Laboratorio Properties of orthogonal matrix and orthogonal transformations Let be an orthogonal square matrix. ]]></description>
    <pubDate>Fri, 05 Jun 2026 19:58:12 GMT</pubDate>
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