Computing Resources
Computing on your own will form an essential part of the learning process and your own applied mathematics training. Matlab is available to all NYU students via our ITS software service or via the Matlab student webpage. You can install it on your personal laptop without a charge.
For beginner programmers I strongly suggest sticking to Matlab so you can focus on numerical analysis and not on programming per se. You are welcome to use Python if you are already familiar with programming and can learn how to use numpy on your own. You are also welcome to use Julia if you are already familiar with it. I will introduce to you symbolic algebra via Maple and the differences between symbolic and numerical computation and the benefits of combining both; you will not be expected to do any Maple programming yourselves but for anyone wishing to do Ph.D. studies in a mathematical field I recommend checking out the student editions of Mathematica (NYU license) or Maple (NYU license).
Do not use sym in Matlab, i.e., do not use symbolic algebra in this class. This class is about computing with floating-point numbers, not symbolic computing, which is an important but distinct tool. If you want to use symbolic algebra, I strongly suggest using Mathematica (e.g. via Wolfram alpha) or Maple (the symbolic algebra in Matlab is just an interface to Maple’s core) or Sage.
Learning Matlab
The recitation leader will provide a Matlab tutorial in the first few sessions. Please go through Chapter 2 in the practice textbook on your own before the semester begins and try to get started in Matlab by doing some of the exercises in that chapter.
For a quick tutorial see the MATLAB Onramp. This is a 2-hour self-paced interactive course on the basics of using MATLAB. The other self-paced courses on that page are freely available to NYU and students can share completion certificates on LinkedIn.
To learn Matlab in more depth, consider these two sources: