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Lecture 1 Part 2: Derivatives as Linear Operators

Course Introduction

Meet the Educator

Lecture 8 Part 1: Derivatives of Eigenproblems

Lecture 2 Part 2: Vectorization of Matrix Functions

Random Matrix Theory and Its Applications

Introduction to Numerical Methods

Parallel Computing

Numerical Methods for Partial Differential Equations (SMA 5212)

Infinite Random Matrix Theory

Principles of Applied Mathematics

Lecture 35: Finding Clusters in Graphs

Lecture 2 Part 1: Derivatives in Higher Dimensions: Jacobians and Matrix Functions

Matrix Calculus for Machine Learning and Beyond

Linear Partial Differential Equations: Analysis and Numerics

Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse

Climate Change Seminar

Lecture 36: Alan Edelman and Julia Language

Introduction to Numerical Analysis for Engineering (13.002J)

Linear Algebra Course Introduction

Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic

Introduction to Computational Thinking

Introduction to Computational Thinking

Making Deep Learning Human with Prof. Gilbert Strang

Gilbert and Cleve Introduction

Lecture 1 Part 1: Introduction and Motivation

Lecture 3 Part 1: Kronecker Products and Jacobians

Visualizing Calculus with Prof. Gigliola Staffilani

Discrete-Time Signal Processing: Oppenheim and Schafer

Numerical Methods for Partial Differential Equations

Gil Strang's Final 18.06 Linear Algebra Lecture

Numerical Methods Applied to Chemical Engineering

Electromagnetism II