Math 9: Introduction to Programming for Numerical Analysis, Winter 2017
Welcome. This course will teach you the basics of programming from a mathematical viewpoint. Programming is an essential skill in modern society, and will open the door for many interesting practical and intellectual endeavours for you. As you will see in the many examples we will consider in this course, computational thinking is a natural extension of mathematical thinking.
We will be using Python in this course. We will rely heavily on Jupyter notebooks for a lot our work. To get started, you should read the guide: Getting set-up for Math 9.
We will not be following a specific textbook in this course. Lecture notes will be made available to give you the information discussed in class. 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.
Assessment consists of:
Weekly homeworks (35%)
Weekly lab assignment (15%, worst two lab results dropped)
Midterm (15%, closed book, on paper, February 13)
Final exam (35%, comprehensive, closed book, on paper, March 20)
Plese see the policy page to see the homework and lab collaboration policy.
Homeworks
The biggest and best time investment you will make in this course will be to do the homework, which will be posted here weekly.
Homework 1, starter code
Homework 2
Homework 3
Homework 4
Homework 5
Homework 6
Homework 7
Homework 8, data and starter code
Homework solutions are available on Canvas.
Exams
Practice Midterm
Midterm
Practice Final
Practice Final Solutions
Piazza page
Please post your questions about the course and homework on Piazza http://piazza.com/uci/winter2017/math9
Lectures
You will need to download the notebooks to your computer and open them in Jupyter.
| Lecture 1 | Jupyter notebooks, expressions, operations, variables | notebook | html |
| Lecture 2 | Variables, Types, Defining your own functions, local vs global variables | notebook | html |
| Lecture 3 | Swapping variables, global keyword, if-else, booleans | notebook | html |
| Lecture 4 | While loops, checking for primeness | notebook | html |
| Lecture 5 | Don’t use == on floats, thinking about algorithm first, division with remainder example | notebook | html |
| Lecture 6 | Checking primes more efficiently, Euclidean Algorithm | notebook | html |
| Lecture 7 | Break and continue, lists, for loops | notebook | html |
| Lecture 8 | List comprehension, mutable vs. immutable | notebook | html |
| Lecture 9 | More on mutables, binary numbers, selection-sort | notebook | html |
| Lecture 10 | Sorting, complexity, big-O notation | notebook | html |
| Lecture 11 | Recursion | notebook | html |
| Lecture 12 | Flattening lists with recursion, map, reduce, filter | notebook | html |
| Lecture 13 | More functional programming methods, map, reduce, filter | notebook | html |
| Lecture 14 | Classes | notebook | html |
| Lecture 15 | Lists of lists, Numpy arrays, matplotlib | notebook | html |
| Lecture 16 | More numpy and matplotlib, plot, showimg, apply along axis | notebook | html |
| Lecture 17 | More about slicing numpy arrays, working with images, historams | notebook | html |
| Lecture 18 | Probability and randomness, random(), randomint | notebook | html |
| Lecture 19 | Choice, mean and std of data-set, random simulations | notebook | html |
| Lecture 20 | Random walks | notebook | html |
| Lecture 21 | Law of large numbers and the central limit theorem | notebook | html |
| Lecture 22 | Minimizing/maximizing functions, Gradient Descent | notebook | html |
| Lecture 23 | Linear Regression | notebook | html |
| Lecture 24 | Solving linear regression by gradient descent | notebook | html |
| Lecture 25 | Singular Value Decomposition, Principal Component Analysis, Eigenfaces | notebook | html |
| Lecture 26 | Matlab tutorial, key differences with Python | notebook | html |
###Office Hours
- Office Location: 510P Rowland Hall (5th floor, turn right when you exit the elevator, then left, 510 is a door on the right past the tutoring center)
- Office hours: Wednesdays 11-12, Thursdays 1-2
###Contact Information
- Umut Isik
- Email: misik@uci.edu
- Phone: (949) 824-3153
###Your TA
- Eric Puttock
- Office hours: Tuesdays 4-6pm at MSTB 226 Lab
- Office: 480 Rowland Hall
###Campus Resources