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Academic Discipline

Mathematics

Linear algebra, real and complex analysis, topology, differential geometry, probability, combinatorics, and number theory.

18courses·132standalone talks·428total lectures

Subfields

All Mathematics428 Calculus & Analysis164 Linear Algebra107 Topology & Geometry59 Category Theory, Topos Theory & Set Foundations56 Probability & Information Theory39 Combinatorics & Graph Theory34 Control & Dynamical Systems31 Convex Optimization21 Formal Methods & Type Theory20 Number Theory & Abstract Algebra18 Statistical Learning & Causality7 Algorithmic Information & Coding Theory5 Cryptography4 Computational Complexity3

Convex Optimization— Convex sets and functions, Lagrangian duality, KKT optimality, interior-point methods, and linear/semidefinite programming.

1 topics

All Convex Optimization Convexity, Duality & Linear Programming10

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Fourier Series: From Joseph Fourier's Heat Equation to Complex Epicycles

Fourier Series: From Joseph Fourier's Heat Equation to Complex Epicycles

Mathematics·Standalone Lecture·Linear Algebra · Grant Sanderson · 3Blue1Brown

Grant Sanderson

24:47

Orthogonal Projections for a Restricted Family of Directions

Orthogonal Projections for a Restricted Family of Directions

Mathematics·Research Seminar·Linear Algebra · Hong Wang · Clay Mathematics Institute

Clay Mathematics Institute

—

55:39

18.02 · Multivariable Calculus · Lec 1·Calculus & Analysis · Denis Auroux · MIT

—

38:40

Transformers & High-Dimensional Vector Geometry of LLMs

Transformers & High-Dimensional Vector Geometry of LLMs

AI & ML·Standalone Lecture·Linear Algebra · Grant Sanderson · 3Blue1Brown

Grant Sanderson

27:14

Orthogonal Matrices and Gram-Schmidt

Orthogonal Matrices and Gram-Schmidt

18.06 · Linear Algebra · Lec 16·Convex Optimization · Gilbert Strang · MIT

—

49:10

Four Ways to Solve Least Squares Problems

Four Ways to Solve Least Squares Problems

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning · Lec 9·Linear Algebra · Gilbert Strang · MIT

—

49:51

Orthonormal Columns in Q Give Q'Q = I

Orthonormal Columns in Q Give Q'Q = I

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning · Lec 3·Convex Optimization · Gilbert Strang · MIT

—

49:24

Projections onto Subspaces

Projections onto Subspaces

18.06 · Linear Algebra · Lec 14·Convex Optimization · Gilbert Strang · MIT

—

48:51

Positive Definite and Semidefinite Matrices

Positive Definite and Semidefinite Matrices

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning · Lec 5·Convex Optimization · Gilbert Strang · MIT

—

45:27

Orthogonal Vectors and Subspaces

Orthogonal Vectors and Subspaces

18.06 · Linear Algebra · Lec 13·Convex Optimization · Gilbert Strang · MIT

—

49:48

Projection Matrices and Least Squares

Projection Matrices and Least Squares

18.06 · Linear Algebra · Lec 15·Convex Optimization · Gilbert Strang · MIT

—

48:05

Minimizing "x" Subject to Ax = b

Minimizing "x" Subject to Ax = b

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning · Lec 11·Convex Optimization · Gilbert Strang · MIT

—

50:22

Chaotic Waterwheel

Chaotic Waterwheel

MAE 5790 · Nonlinear Dynamics and Chaos · Lec 15·Linear Algebra · Steven Strogatz · Cornell University

Steven Strogatz

Cornell University

—

1:14:12

Linear Transformations in Geometry

Linear Transformations in Geometry

Linear Algebra: Systems, Matrices & Transformations · Lec 7·Convex Optimization · Valerie Hower · Florida International University

Florida International University

—

1:10:03

Max-Min Problems; Least Squares

Max-Min Problems; Least Squares

18.02 · Multivariable Calculus · Lec 9·Linear Algebra · Denis Auroux · MIT

—

49:44

Linear Algebra: Extra Practice Worksheet 3

Linear Algebra: Extra Practice Worksheet 3

Linear Algebra: Systems, Matrices & Transformations · Lec 12·Convex Optimization · Valerie Hower · Florida International University

Florida International University

—

17:10

Showing 16 of 16 lectures·12.5h shown

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Linear Algebra · Convex Optimization

16