# Harvard AM111 2017 Fall¶

**Course Name**: Applied Math 111, Introduction to Scientific Computing

**Instructor**: Prof. Robin Wordsworth (rwordsworth@seas.harvard.edu), 426 Geological Museum

**Teaching Fellow**: Jiawei Zhuang (jiaweizhuang@g.harvard.edu), Pierce Hall 108

**Lecture Time**: Tues/Thurs 13:00-14:30

**Lecture Location**: 375 Geological Museum (3rd floor)

**Session Time**: Every Monday 17:00-18:00

**Session Location**: GeoMuseum 103A

**TF’s Office Hour**: Every Tuesday 15:00-16:00, Pierce Hall 108

This website contains supplemental materials made by the TF, Jiawei Zhuang.
These materials are **not required** for completing this course,
but just provide additional information I find useful. Might also use them for the session.
Your grade will **not be affected** if you choose to ignore this website.

## Lecture & Session Notes¶

Forget the coding exercises in the class? The following notes might help.

- Lecture 2: Logic Gates & Fibonacci Numbers
- Lecture 4: Floats & Random Numbers
- Lecture 5: Random Numbers & Complex Numbers
- Lecture 6: Matrix
- Lecture 8: Interpolation
- Lecture 11: Odyssey!!!
- Session 1: MATLAB Functions and Scripts
- Session 2: Speed-up your code by vectorization
- Session 3: LU Factorization & Markov Process
- Session 4: Linux Command Line
- Session 5: MATLAB backslash & some pitfalls
- Session 6: Three ways of differentiation
- Session 7: Error convergence of numerical methods
- Session 8: ODE stability; stiff system
- Session 9: Partial differential equation
- Session 10: Fast Fourier Transform

These notes combine codes and results together. You can just copy the codes to your MATLAB console or script.

If you wonder how I wrote codes in this format and want to try it yourself, see the section below.

## MATLAB in Jupyter Notebooks¶

This course is taught in MATLAB. One thing you could try is to run MATLAB codes in Jupyter Notebooks. Jupyter Notebook is a must-learn tool for data scientists, and it is also becoming popular in the traditional scientific computing community. It is a great tool for interactive computing and allows you to combine codes, simulation results, and descriptions such as latex equations in a single file.

See how to install and use it at:

Some basic understanding of Linux command line and Python will be useful for installation.
**If you are just new to programming, you should simply use MATLAB’s original, basic user interface
and come back to this tutorial later when you are interested.**

If you feel good about this tool, you can choose to submit notebooks for your homework,
Again, **your grade will not be affected by the file format of your homework**.