MATH 3511
Spring semester 2021
  • Syllabus
  • Calendar
    • Homework guidelines
    • HW01
    • HW02
    • HW03
    • HW04
    • HW05
    • HW06
    • HW07
    • Homework grades
  • Downloads
    • Midterm 1
    • Midterm 2
    • Final exam
    • Exam grades
  • HuskyCT

Julia Programming

Julia is a high-level, high-performance programming language for numerical computing


Why we created Julia

by Jeff Bezanson, Stefan Karpinski, Viral Shah, Alan Edelman


Julia cheatsheet


Julia manual


Julia tutorials

Toggle the list »

  • Carsten Bauer, Julia Workshop for Physicists , Feb. 2020

  • Jesse Perla, Thomas J. Sargent, and John Stachurski, Quantitative economics with Julia , May 2020

    The topics of the lecture series include:

    1. Basics of coding skills and software engineering
    2. Algorithms and numerical methods
    3. Related mathematical and statistical concepts
    The intended audience is undergraduate students, graduate students and researchers in any field, not restricted to economics

  • Aurelio Amerio, From zero to Julia! , Spring 2020

    A small series of introductory lessons to the Julia language. The aim of this course is to give you the basics to be able to start coding in Julia on your own.

  • wikibooks.org, Introducing Julia

  • Bogumil Kaminski, The Julia Express

  • Samuel Colvin, Julia by Example

  • Jesse Perla, Thomas J. Sargent and John Stachurski, Quantitative Economics with Julia , May 15, 2020

  • Antonello Lobianco, Julia language: a concise tutorial

  • MIT 6.S083 /18.S190, Introduction to computational thinking with Julia , Spring 2020

  • Julia language: youtube channel

  • Jane Herriman, Intro to Julia, Nov 2018


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Etiam porta sem malesuada magna mollis euismod rendered as bold text. Cras mattis consectetur purus sit amet fermentum le syndrome du clandestin. A clear, authoritative judicial holding on the meaning of a particular

\[ \begin{align} \nabla \times \vec{E} & = -\frac{\partial\vec{B}}{\partial t} \\ \nabla \cdot \vec{D} & = \rho_f \\ \nabla \times \vec{H} & = \vec{J}_f + \frac{\partial\vec{D}}{\partial t}\\ \nabla \cdot \vec{B} & = 0 \end{align} \]

provision should not be cast in doubt and subjected to challenge whenever a related though not utterly inconsistent provision is adopted in the same statute or even in an affiliated statute, the two authors wrote

Resources

  1. Course textbook on author's website
  2. Course textbook on Google Books
  3. Course textbook, Julia version
  4. Numerical computing
  5. Matlab
  6. Git and Gitlab
  7. Latex
  8. Julia

Course Archives

  1. Numerical Analysis I, Fall 2020
  2. Numerical Analysis II, Spring 2020

Links

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  6. Academic Calendar, Spring 2021
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