18.330: Introduction to Numerical Analysis
Spring 2021
Welcome to course 18.330 at MIT! This is an introductory course on numerical analysis.
Installation of required software
We will be using the following free / open source software:
- The Julia language
- The Pluto notebook environment
Please follow these instructions to install Julia and then Pluto.
Logistics
-
Visiting professor David P. Sanders (sandersd@mit.edu)
-
TA: Nicholas Liu
-
Tues, Thurs 1–2:30pm Eastern, virtual
-
Office hours: Tues, 5–7pm Eastern
Course materials
-
Lectures are synchronous Zoom sessions for MIT students
-
Notes are public:
Evaluation
- 10 problem sets (50%). No late submissions, but the lowest score will be dropped.
- 1 midterm take-home exam (20%)
- Final project (30%)
Problem sets will consist of a mixture of theory and coding in Julia. They will be submitted and graded online.
For the final project you will explore a topic in numerical analysis that we have not covered in class (but at the level of the class). The final project must include a discussion of the mathematics behind the method, together with your own implementation in Julia.
Learning Julia
-
Prof. Steven Johnson will give an introduction to Julia on Friday, Feb 19 from 5-7pm. Make sure to install Julia beforehand. See https://github.com/mitmath/julia-mit for information and resources on Julia.
-
More learning resources are available at https://julialang.org/learning/
Windows users
If you use Windows, please download Git for Windows here
Getting the files
To get the files, use git from the command line (or from a GUI), as follows
- Clone the repository once with
git clone https://github.com/mitmath/18330
This will create a new directory called 18330 with the matierials.
- Update it to pull in new changes each time with
git pull
This needs to be executed from within the directory. (Use cd to change directory.)
Syllabus
See here for the approximate course syllabus.
Bibliography
See here for bibliography and related online courses and learning materials.