MIT OpenCourseWare · Education

MIT OpenCourseWare

A free and open online publication of educational material from thousands of MIT courses, covering the entire MIT curriculum, ranging from introductory to the most advanced graduate courses. On the OCW website, each course includes a syllabus, instructional material like notes and reading lists, and learning activities like assignments and solutions. Some courses also have videos, online textbooks, and faculty insights on teaching. Knowledge is your reward. There's no signup or enrollment, and no start or end dates. OCW is self-paced learning at its best. Whether you’re a student, a teacher, or simply a curious person that wants to learn, MIT OpenCourseWare (OCW) offers a wealth of insight, inspiration, videos, and a whole lot more! Get the full picture on the OCW website at https://ocw.mit.edu. Accessibility: https://accessibility.mit.edu/ User comments policy: https://ocw.mit.edu/comments/ (Channel banner photo by Nietnagel on Flickr: https://flic.kr/p/8WXxfK.)

MIT OpenCourseWare

Episodes

Supplemental Video: Ternary Phase Diagram and Ouzo Demo

Lecture 23: Building Binary Phase Diagrams, Part I

Lecture 14: Reacting Gas Mixtures at Equillibrium

Lecture 10: Introduction to Unary Phase Transformations

Lecture 8: Mathematical Implications of Equilibrium and Spontaneous Processes

Lecture 1: Introduction to Thermodynamics

Lecture 1 Part 1: Introduction and Motivation

Lecture 8 Part 1: Derivatives of Eigenproblems

Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse (old)

Lecture 3 Part 1: Kronecker Products and Jacobians

Lecture 3 Part 2: Finite-Difference Approximations

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

Lecture 4 Part 2: Nonlinear Root Finding, Optimization, and Adjoint Gradient Methods

Lecture 7 Part 2: Second Derivatives, Bilinear Forms, and Hessian Matrices

Lecture 4 Part 1: Gradients and Inner Products in Other Vector Spaces

Lecture 2 Part 2: Vectorization of Matrix Functions

Lecture 1 Part 2: Derivatives as Linear Operators

Lecture 8 Part 2: Automatic Differentiation on Computational Graphs

Lecture 7 Part 1: Derivatives of Random Functions

Lecture 6 Part 1: Adjoint Differentiation of ODE Solutions