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Transformations - Composability and Linearity in Computational Thinking - Lecture 4

Offered By: The Julia Programming Language via YouTube

Tags

Julia Courses Image Processing Courses Computational Thinking Courses Linear Transformations Courses

Course Description

Overview

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.

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

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