Mathematics

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

11 courses · 160 standalone talks · 518 total lectures

All Mathematics Calculus & Analysis (204)Linear Algebra (105)Probability & Information Theory (105)Control & Dynamical Systems (68)Topology & Geometry (50)Signal Processing & Robotics (48)Number Theory & Abstract Algebra (39)Algorithms & Data Structures (32)multivariable calculus (28)Real Analysis (27)

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Chapters · Duration

Convex Optimization with Abstract Linear Operators

Convex Optimization with Abstract Linear Operators

Mathematics·Conference Talk · Stephen P. Boyd · Stanford University

Stephen P. Boyd Stanford University

1:04:29

Private Adaptations of Large Language Models

Private Adaptations of Large Language Models

AI & ML·Research Seminar · Adam Dziedzic · Caltech

Adam Dziedzic Caltech

50:30

Leveraging Per-Instance Privacy for Machine Unlearning

Leveraging Per-Instance Privacy for Machine Unlearning

Mathematics·Conference Talk · Anvith Thudi · Google Research & TechTalks

Anvith Thudi Google Research & TechTalks

33:23

Convex Functions

Convex Functions

Mathematics·Standalone Lecture · Steve Brunton · University of Washington

Steve Brunton University of Washington

16:35

Proof: All Norms Are Convex

Proof: All Norms Are Convex

Theory & Algos·Standalone Lecture · Steve Brunton · University of Washington

Steve Brunton University of Washington

18:51

Convexity and Global Minima in Optimization

Convexity and Global Minima in Optimization

Mathematics·Standalone Lecture · Steve Brunton · University of Washington

Steve Brunton University of Washington

20:26

Lecture 02: How to Train a Neural Net (6.7960)

Lecture 02: How to Train a Neural Net (6.7960)

6.7960 · Lec 2 · Sara Beery · MIT

1:19:34

Lecture 12: Introduction to Linear Programming (18.200)

Lecture 12: Introduction to Linear Programming (18.200)

18.200 · Lec 12 · Peter Shor · MIT

1:14:04

Lecture 15: Max-Flow Min-Cut Theorem (18.200)

Lecture 15: Max-Flow Min-Cut Theorem (18.200)

18.200 · Lec 15 · Peter Shor · MIT

1:17:52

Lecture 13: Duality in Linear Programming (18.200)

Lecture 13: Duality in Linear Programming (18.200)

18.200 · Lec 13 · Peter Shor · MIT

1:16:12

Lecture 3: Subgradient Methods (EE364B)

Lecture 3: Subgradient Methods (EE364B)

EE364a · Lec 22 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:14:45

Lecture 2: Subgradients Continued (EE364B)

Lecture 2: Subgradients Continued (EE364B)

EE364a · Lec 21 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:07:27

Lecture 1: Subgradients (EE364B)

Lecture 1: Subgradients (EE364B)

EE364a · Lec 20 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:01:37

5. Positive Definite and Semidefinite Matrices

5. Positive Definite and Semidefinite Matrices

18.065 · Lec 5 · Gilbert Strang · MIT

Gilbert Strang MIT

45:27

Lecture 20: Option Price and Probability Duality (18.S096)

Lecture 20: Option Price and Probability Duality (18.S096)

18.S096 · Lec 19 · Stephen Blyth · MIT

Stephen Blyth MIT

1:20:29

Lecture 14: Numerical Linear Algebra Background (EE364a)

Lecture 14: Numerical Linear Algebra Background (EE364a)

EE364a · Lec 14 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:10:12

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