README.md

Math 9 Introduction to Programming for Numerical Analysis

Instructor: Peter McHale

Course webpage: https://eee.uci.edu/17f/44600

Getting set up (the software below is FREE!)

In what follows, you will need to access the 'command line'. On a Mac, this is done by opening the Terminal app. On the lab (Windows) machines, this is done via Start -> Anaconda Prompt (type this into the search field to locate the program). Your TA will help you with this.

If on your own machine, install Python and Jupyter by installing Anaconda (Python 3.x version). Anaconda conveniently installs Python, the Jupyter Notebook, and other commonly used packages for scientific computing. Please type

conda create -n math9 python=2 ipython-notebook --yes

at the Terminal (Mac) or Anaconda (Windows) prompt to create a conda environment using Python 2. Then activate the environment. Your TA will help you with this.

If you are working at a lab computer, which already has Python and Jupyter installed, then type python --version at the command prompt to check the version of Python that is installed. It will hopefully say Python 2.x, which is what we will use in this course.

Open a Jupyter notebook by navigating to the directory in which it is located (the cd command is useful here, as is the ls command in Terminal or equivalently, the dir command in Windows) and typing jupyter notebook at the command prompt. A tab will open in your browser revealing the contents of the current directory. Seek out the TA for help.

Once you’re finished editing/running your notebook, press ctrl-c twice at the command prompt.

If Jupyter complains that a specific package is missing when you run your notebook, then return to the command line, execute conda install <name of package>, and re-run the offending notebook cell.

PLEASE BRING USB DRIVE TO LAB TO SAVE YOUR WORK.

Acknowledgements

This course is adapted from Umut Isik's course

Book

We will not be following a specific textbook in this course. However, if you would like to read a book to help you with the course, I recommend: Scientific Computation: Python Hacking for Math Junkies, by B. Shapiro.

Schedule

If time permits, I will try to indicate relevant sections of Shapiro's book in the column entitled Sections.

Click on the links to see nbviewer-rendered versions of the lecture.

Wk	Date	Lec	Sections	Topics
0	9/29	1		Markdown vs code cell, math operators, library functions, strings
1	10/2	2		Variables, types, functions
	10/4	3		Tracking variables, graphing, if-else, comparisons, boolean ops
	10/6	4		While loops, checking for primeness
2	10/9	5		Don't use == on floats, division with remainder case study
	10/11	6		Checking primes more efficiently, Euclids Algorithm
	10/13	7		Break and continue, lists
3	10/16	8		List comprehension, mutable vs. immutable
	10/18	9		More on mutable vs immutable, selection-sort
	10/20	10		Selection sort, algorithm complexity
4	10/23	11		Recursion
	10/25	12		HW03/Discussion session
	10/27	13		Flattening lists with recursion; map, reduce, filter
5	10/30			Review of previous exams
	11/1		L1 - L13	Midterm Exam
	11/3	14		Classes
6	11/6	15		Lists of lists, Numpy arrays, matplotlib
	11/8	16		More numpy and matplotlib, faces dataset
	11/10		No class	Veterans’ Day
7	11/13	17		Slicing, images, histograms
	11/15	18		Probability and randomness
	11/17	19		Choice, mean and std of data-set, fitting data
8	11/20	20		Random walks
	11/22	21		Law of large numbers and the central limit theorem
	11/24		No class	Thanksgiving
9	11/27	22		Minimizing/maximizing functions, Gradient Descent
	11/29	23		Linear Regression
	12/1	24		Solving linear regression by gradient descent
10	12/4	25		Singular Value Decomposition, Principal Component Analysis, Eigenfaces
	12/6	26		Matlab tutorial, key differences with Python
	12/8	27		Review of previous exams
11	12/11		L1 - L27	Final Exam 10.30am – 12.30pm