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Computational Fundamentals for Machine Learning - CST 294 KTU Honors Notes

Introduction About Me Syllabus University Question Papers July 2021- Aug 2022 Overview of Machine Learning What is Machine Learning (video) Learn the Seven Steps in Machine Learning (video) Module I- Linear Algebra 1.Geometry of Linear Equations (video) 2.Elimination with Matrices (video) 3.Solving System of equations using Gauss Elimination Method 4.Row Echelon form and Reduced Row Echelon Form 5.Solving system of equations Python code 6. Practice problems Gauss Elimination ( contact) 7.Finding Inverse using Gauss Jordan Elimination  (video) 8.Finding Inverse using Gauss Jordan Elimination-Python code 9.Vector spaces and sub spaces 10.Linear Independence 11.Linear Independence, Basis and Dimension (video) 12.Generating set basis and span 13.Rank of a Matrix 14.Linear Mapping and Matrix Representation of Linear Mapping 15.Basis and Change of basis 16. Transformation Matrix in new Basis 17.Image and Kernel 18.Example Problems ( contact) Module-II -Analytic  Geometry and Matrix Decomposi
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University Question Papers Computational Fundamentals for Machine Learning - CST 294 KTU

 
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Syllabus Computational Fundamentals for Machine Learning - CST 294 KTU

  Syllabus Module 1 LINEAR ALGEBRA : Systems of Linear Equations – Matrices, Solving Systems of Linear Equations. Vector Spaces –Vector Spaces, Linear Independence, Basis and Rank. Linear Mappings –Matrix Representation of Linear Mappings, Basis Change, Image and Kernel. Module 2  ANALYTIC GEOMETRY, MATRIX DECOMPOSITIONS : Norms, Inner Products, Lengths and Distances, Angles and Orthogonality, Orthonormal Basis, Orthogonal  Complement, Orthogonal Projections – Projection into One Dimensional Subspaces, Projection onto General Subspaces, Gram-Schmidt Orthogonalization. Determinant and Trace, Eigenvalues and Eigenvectors, Cholesky Decomposition, Eigen decomposition and Diagonalization, Singular Value Decomposition, Matrix Approximation. Module 3 VECTOR CALCULUS :  Differentiation of Univariate Functions - Partial Differentiation and Gradients, Gradients of Vector Valued Functions, Gradients of Matrices, Useful Identities for Computing Gradients. Back propagation and Automatic Differentia
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