session introduction

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1. 1▶Session 10: Gradient descent, why it works, Linear and Logistic regression, ML estimateJadavpur University: Foundation_Math_forML_Autumn23Notes
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2. 2▶Session 9: Introduction to convex functions, Jensen’s, Holder’s inequality, Minkowski, LagrangianJadavpur University: Foundation_Math_forML_Autumn23Notes
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3. 3▶Session 8: Inner products, vector norms, dual spaces, introduction to matrix normsJadavpur University: Foundation_Math_forML_Autumn23Notes
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4. 4▶Session 7: Eigenvector decomposition, unitary and normal matrices, Application: PCA and SVDJadavpur University: Foundation_Math_forML_Autumn23Notes
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5. 5▶Session 6: Projections, Least squares, eigenvalue-eigenvectors, Char. polynomial, similar matricesJadavpur University: Foundation_Math_forML_Autumn23Notes
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6. 6▶Session 5: Intro to Matrices, vector spaces, span and basis, 4-fundamental subspaces, eliminationJadavpur University: Foundation_Math_forML_Autumn23Notes
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7. 7▶Session 4: MGFs, random vectors, joint distribution, random process, Random walks, Markov chainsJadavpur University: Foundation_Math_forML_Autumn23Notes
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8. 8▶Session 3: continuous rvs, pmf, pdf, inequality, Uniform, Exponential, Normal, transformation of rvsJadavpur University: Foundation_Math_forML_Autumn23Notes
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9. 9▶Session 2: Discrete random variables, distribution, expectation, lotus, varianceJadavpur University: Foundation_Math_forML_Autumn23Notes
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10. 10▶Session 1: Introduction to counting, RVs, and distributionsJadavpur University: Foundation_Math_forML_Autumn23Notes
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