Explore transformations, composability, and linearity in this MIT Computational Thinking Spring 2021 lecture. Dive into image processing as a tool for learning Julia programming, examine perspective maps and linear perspective interactively, and master advanced Julia techniques for defining vector-valued functions. Discover various linear and nonlinear transformations, understand function composition, and grasp the distinction between sin and sin(x). Conclude with a comprehensive definition of linear transformations, setting the stage for future discussions.
Transformations - Composability and Linearity in Computational Thinking - Lecture 4
The Julia Programming Language via YouTube
Overview
Syllabus
Introduction.
Playing with transformations.
Why Image Processing to learn Julia?.
Last lecture leftovers: Perspective maps, Linear perspective interactive.
Julia style(advanced): Defining vector valued functions.
Functions with parameters.
Linear transformations: a collection.
Nonlinear transformations: a collection.
Composition.
Difference between sin and sin(x).
Definition of Linear Transformations.
To be discussed in next lecture.
Taught by
The Julia Programming Language
