Beginner Lessons

Lecture 23: Visualizing Data

Lecture 01: Introduction to 14.310x Data Analysis for Social Scientists

Lecture 09: Expectation, Variance, and Introduction to Regression

Lecture 13: Introducing the Dirac Equation

Lecture 26: Quantum Fluctuations and Renormalization

Lecture 7: Interacting Theories and S-Matrix

Lecture 17: Chiral and Majorana Spinors

Lecture 2: Pronunciation, Grammar Review, the Case System, and Word-Formation

Lecture 8: Syntax, Part 1

Lecture 6: Mock Exam, Verbs, Part 2, and Adjectives

Lecture 5: Verbs, Part 1

Lecture 4: Nouns

Lecture 7: Numerals, Adverbs, Conjunctions, Prepositions, Concord, and Word Order

Lecture 3: Pronouns

Five Factorizations of a Matrix

Lecture 5 Part 1: Derivative of Matrix Determinant and Inverse

Lecture 5 Part 2: Forward Automatic Differentiation via Dual Numbers

Lecture 5 Part 3: Differentiation on Computational Graphs

Supplemental Video: Ternary Phase Diagram and Ouzo Demo

Lecture 18: Case Study in Reacting Gas Mixtures - Introducing the Nernst Equation

Lecture 27: Introduction to Statistical Thermodynamics

Lecture 10: Introduction to Unary Phase Transformations

Lecture 15: Introduction to Solutions, General Case

Lecture 19: Regular Solution Models and Stability

Lecture 13: Introduction to Ideal (Gas) Mixtures

Lecture 3: Process Variables and the First Law

Lecture 1 Part 1: Introduction and Motivation

Lecture 8 Part 1: Derivatives of Eigenproblems

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

Lecture 6 Part 2: Calculus of Variations and Gradients of Functionals

Lec14: Communication and Meerkats of the Kalahari Desert, part 1

Lec9: Lorenz on fundamental ethology, part 1

Lec29: Konrad Lorenz on learning, part 2

Lec2: Introduction to ethology

Lec28: Triumph of sociobiology and learning

Lec1: Introduction to animal behavior and how it is studied

Lec30: Konrad Lorenz on learning, part 3

15. Motor systems and brain states, part 1