matrice: videos

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1. 1▶CENG 465 - Intro to Bioinformatics - Profile Hidden Markov Models #2CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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2. 2▶CENG 465 - Intro to Bioinformatics - Position Specific Scoring Matrices #2, Hidden Markov Models #1CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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3. 3▶CENG 465 - Intro to Bioinformatics - Profiles, Position Specific Scoring MatricesCENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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4. 4▶CENG 465 - Intro to Bioinformatics - Suffix Trees, Suffix ArraysCENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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5. 5▶CENG 465 - Intro to Bioinformatics - Statistical Significance Computation Example, Suffix TreesCENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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6. 6▶CENG 465 - Intro to Bioinformatics - Statistical Significance of Alignments, Scoring Matrices #2CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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7. 7▶CENG 465 - Intro to Bioinformatics - Statistical Significance of Alignments, Scoring MatricesCENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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8. 8▶CENG 465 - Intro to Bioinformatics - BLAST and Statistical Significance of AlignmentsCENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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9. 9▶CENG 465 - Intro to Bioinformatics - Pairwise Sequence Alignment #4CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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10. 10▶CENG 465 - Intro to Bioinformatics - Pairwise Sequence Alignment #3CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)Notes
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11. 11▶6271 16 EstimatingRandomVectorsAdaptive Signal ProcessingNotes
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12. 12▶6271 15 OptimalEstimatorsAdaptive Signal ProcessingNotes
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13. 13▶6271 14 BayesRuleAdaptive Signal ProcessingNotes
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14. 14▶6271 13 EstimatingRandomVariablesPt1Adaptive Signal ProcessingNotes
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15. 15▶6271 12 RandomVectorsAdaptive Signal ProcessingNotes
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16. 16▶6271 11 MoreMatrixMathAdaptive Signal ProcessingNotes
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17. 17▶6271 10 SpectralDecompositionAdaptive Signal ProcessingNotes
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18. 18▶6271 09 Hermitian MatricesAdaptive Signal ProcessingNotes
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19. 19▶6271 08 FilteringRandomSignalsAdaptive Signal ProcessingNotes
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20. 20▶6271 07 EstimatingPSDAdaptive Signal ProcessingNotes
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21. 21▶Session 10: Gradient descent, why it works, Linear and Logistic regression, ML estimateJadavpur University: Foundation_Math_forML_Autumn23Notes
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22. 22▶Session 9: Introduction to convex functions, Jensen’s, Holder’s inequality, Minkowski, LagrangianJadavpur University: Foundation_Math_forML_Autumn23Notes
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23. 23▶Session 8: Inner products, vector norms, dual spaces, introduction to matrix normsJadavpur University: Foundation_Math_forML_Autumn23Notes
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24. 24▶Session 7: Eigenvector decomposition, unitary and normal matrices, Application: PCA and SVDJadavpur University: Foundation_Math_forML_Autumn23Notes
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25. 25▶Session 6: Projections, Least squares, eigenvalue-eigenvectors, Char. polynomial, similar matricesJadavpur University: Foundation_Math_forML_Autumn23Notes
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26. 26▶Session 5: Intro to Matrices, vector spaces, span and basis, 4-fundamental subspaces, eliminationJadavpur University: Foundation_Math_forML_Autumn23Notes
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27. 27▶RM+ML: 15. Spiked Signal-Plus-Noise ModelHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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28. 28▶Session 4: MGFs, random vectors, joint distribution, random process, Random walks, Markov chainsJadavpur University: Foundation_Math_forML_Autumn23Notes
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29. 29▶RM+ML: 14. Proof of Marchenko-Pastur: Stieltjes Inversion FormulaHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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30. 30▶RM+ML: 13. Proof of Marchenko-Pastur: Equation for Stieltjes TransformHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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31. 31▶RM+ML: 12. Preparations for Proof of Marchenko-Pastur LawHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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32. 32▶RM+ML: 11. The Marchenko-Pastur Law for Wishart MatricesHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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33. 33▶Session 3: continuous rvs, pmf, pdf, inequality, Uniform, Exponential, Normal, transformation of rvsJadavpur University: Foundation_Math_forML_Autumn23Notes
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34. 34▶Session 2: Discrete random variables, distribution, expectation, lotus, varianceJadavpur University: Foundation_Math_forML_Autumn23Notes
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35. 35▶Session 1: Introduction to counting, RVs, and distributionsJadavpur University: Foundation_Math_forML_Autumn23Notes
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36. 36▶RM+ML: 10. Proof of Concentration of Largest EigenvalueHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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37. 37▶RM+ML: 9. Wishart Random Matrices and Concentration of Largest EigenvalueHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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38. 38▶RM+ML: 8. General Remarks on Linear and Non-Linear Concentration InequalitiesHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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39. 39▶RM+ML: 7. Proof of Non-Linear Concentration for Gaussian Random VectorsHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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40. 40▶RM+ML: 6. Non-Linear Concentration of Gaussian Random Vectors for Lipschitz Functions.High Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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41. 41▶RM+ML: 5. Exponential Concentration of Norm of Gaussian Random VectorsHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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42. 42▶RM+ML: 4. Gaussian Random Vectors and Concentration of Their NormHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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43. 43▶RM+ML: 3. Concentration of VolumesHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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44. 44▶RM+ML: 2. Volumes in High DimensionsHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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45. 45▶RM+ML: 1. Introduction and SurveyHigh Dimensional Analysis: Random Matrices and Machine Learning (RM+ML)Notes
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46. 46▶Quantum Computing: Algorithm, Programming and Hardware, an IntroductionQuantum Computing Hardware and Architecture (EE274 SJSU, 2023)Notes
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47. 47▶L6-1 Review of Lecture 5 and Orthogonality of Pauli MatricesQuantum Computing Hardware and Architecture (EE274 SJSU, 2023)Notes
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48. 48▶L5-2 Pure and Mixed States and Density Matrix Part 1Quantum Computing Hardware and Architecture (EE274 SJSU, 2023)Notes
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49. 49▶L5-1 Pauli Matrices and Inner Product of MatricesQuantum Computing Hardware and Architecture (EE274 SJSU, 2023)Notes
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50. 50▶L4-4 Pauli Matrices Part IQuantum Computing Hardware and Architecture (EE274 SJSU, 2023)Notes
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