|
..
|
|
Lecture 01 - Systems of linear equations, vectors, matrices, Gauss Elimination and LU-factorization.pdf
|
|
Lecture 02 - Pivots + Permutations, Matrix Inverses (Gauss-Jordan), Transposes, Symmetric Matrices, and General Linear Systems.pdf
|
|
Lecture 03 - Vector Spaces 1_ Definitions, Subspaces, Span, Linear Independence, Basis, and Dimension.pdf
|
|
Lecture 04 - The Fundamental Matrix Subspaces (Kernel, Image, CoKernel, CoImage), Fundamental Theorem of Linear Algebra, and a brief interlude on the Matrix Transpose.pdf
|
|
Lecture 05 - Inner products, length, angles, and norms.pdf
|
|
Lecture 06 - Clustering and K-means.pdf
|
|
Lecture 07 - Orthogonality, Gram-Schmidt, Orthogonal Matrices, and QR-Factorization.pdf
|
|
Lecture 08 - Orthogonal Projections and Subspaces, Least Squares Problems and Solutions.pdf
|
|
Lecture 09 - Least Squares Data Fitting.pdf
|
|
Lecture 10 - Linearity, Linear Functions, Transformations, and Operators.pdf
|
|
Lecture 11 - Eigvenvalues and Eigenvectors part 1 (dynamical systems, determinants, basic definitions and computations).pdf
|
|
Lecture 12 - Eigvenvalues and Eigenvectors part 2 (complex eigenvalues and eigenvectors, similarity transformation, diagonalization and eigenbases).pdf
|
|
Lecture 13 - Complex and Repeated Eigenvalues Revisited, Jordan Blocks, Matrix Exponential.pdf
|
|
Lecture 14 - Invariant Subspaces, Inhomogeneous Systems, and Applications to Mechanical Systems.pdf
|
|
Lecture 15 - Linear Iterative Systems, Matrix Powers, Markov Chains, and Google’s PageRank.pdf
|
|
Lecture 16 - Eigenvalues of Symmetric Matrices, Spectral Theorem, Quadratic Forms and Positive Definite Matrices, Optimization Principles for Eigenvalues of Symmetric Matrices.pdf
|
|
Lecture 17 - Introduction to Graph Theory and Consensus Protocols.pdf
|
|
Lecture 18 - Singular Values and the Singular Value Decomposition.pdf
|
|
Lecture 19 - Principal Component Analysis with Applications to Imaging and Data Compression.pdf
|
|
Lecture 20 - Low-Rank Matrix Approximations via the SVD with applications to matrix completion and recommender systems.pdf
|
|
Lecture 21 - An introduction to unconstrained optimization, gradient descent, and Newton’s method.pdf
|
|
Lecture 22 - An Introduction to Backpropagation.pdf
|
|
Lecture 23 - Stochastic Gradient Descent, Linear Classification and the Perceptron Algorithm, Multilayer Perceptrons.pdf
|