Schedule - Fall 2020

This Schedule is for Fall 2020. The website for the Fall 2021 version of the class is here. This page will be updated for Winter 2022.

This course follows a Monday, Wednesday, Friday schedule. There is a section for each day, with materials for that day. This schedule is subject to change before a class is held.

Day 00 - 9/30

Class Material

Slides for course introduction (use UChicago login). [Zoom Recording - first 20 min are slides, second 25 min are questions]

1. Basic Bash [Video Walkthrough]
2. Install Anaconda Python [Video Walkthrough]
3. Install Jupyter notebooks [Video Walkthrough]
4. Python Basics [Video]

Reading

• Discrete or Continuous? by N. Trefethen. Required
• Top-10 Algorithms of the 20th Century by B. Cipra. Required
• What Kind of Science is Computational Science? by R. Allain Recommended
• and A Rebuttal by T. Kolda. Recommended
• A Science-Based Case for Large-Scale Simulation US Department of Energy Optional

Day 01 - 10/2

Homework

• see the git tutorial if you are not familiar with git version control. [Video]
• Homework 0 released

Class Material

1. Bits, Bytes, and Numbers [Video]
2. Basic Containers and Packages [Video]
3. Asymptotic notation [Video]
4. Python Scripts [Download] [Video Walkthrough]

Reading

• PEP 0020 - The Zen of Python Required
• PEP 0008 - Style Guide (just skim and read anything interesting) Required
• Array Programming with NumPy by Harris, et al. Recommended

Day 02 - 10/5

Class Material

1. Functions in Python [Video]
2. Decorators [Video]
3. Recursion [Video]

Reading

• Functions Required
• More on defining functions Required
• Function definitions Recommended

Day 03 - 10/7

Class Material

1. Python Objects, OOP [Video]
2. Modules and Packages [GitHub repository] [Video]

Reading

• Classes Required at least through 9.5 (inheritance)
• Class definitions Recommended
• Modules Required

Day 04 - 10/9

Homework

• Homework 0 due - 12pm CST
• Homework 1 released

If you are still new to git, see collaboriating with git [Video]

Class Material

1. Convergence of Algorithms [Video]
2. Root Finding [Video]

Reading

• Newton’s Method on Wolfram Mathworld Recommended

Day 05 - 10/12

Class Material

1. Vectorization, numpy ufuncs, numba [Video]
2. Memory layout [Video]

Reading

• Mastering SciPy pp 13 - 18 (Creation of matrices) required
• NumPy Ufuncs Recommended

Day 06 - 10/14

Class Material

1. Dense Linear Algebra [Video]

If you don’t have much prior experience with matrix factorizations, it is highly recommended to go through the exercises in the notebook.

Reading

• Mastering SciPy pp 28 - 38 (Basic Matrix Manipulation) required
• Mastering SciPy pp 38 - 41 (Matrix Factorizations) required
• Mastering SciPy pp 54 - 55 (Eigenvalue Decompositions) required

Day 07 - 10/16

Homework

• Homework 1 due
• Homework 2 released

Class Material

1. SciPy BLAS and LAPACK Interfaces [Video]

Reading

• LAPACK on netlib Recommended
• BLAS on netlib Recommended

Day 08 - 10/19

Class Material

1. Sparse matrix formats, scipy.sparse [Video]
2. Linear operators [Video]

Reading

• Mastering SciPy pp 19 - 28 (Creation of sparse matrices, linear operators) Required

Day 09 - 10/21

1. Sparse Linear Algebra [Video]

Sparse direct methods, iterative methods, ARPACK, randomized linear algebra.

Reading

• Mastering SciPy pp 44 - 51 (Sparse direct, iterative methods) Required
• Mastering SciPy pp 56 - 57 (eigs, eigsh) Required
• Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions (Halko, Martinsson, Tropp. 2009) Recommended - you can just skim and read the algorithms on a first pass.

Day 10 - 10/23

Homework

• Homework 2 due
• Homework 3 released

Class Material

1. Python Iterators and Generators [Video]
2. Agent-based modeling [Video]

Reading

• Python Tutorial on Iterators Required
• Python Tutorial on Generators Required
• E. Bonabeau Agent-based modeling: Methods and techniques for simulating human systems (2002) Recommended

You may also want to look at the Wikipedia entry for Agent-based model

Day 11 - 10/26

Class Material

1. Symbolic Computing with SymPy [Video]
2. Differentiation [Video]

Reading

• SymPy Introduction Required Look around the documentation as well.
• Mastering SciPy pp 105 - 110 (Differentiation) Required

Day 12 - 10/28

Class Material

1. Initial Value Problems [Video]

Reading

• Mastering SciPy pp 165 - 178 (Initial Value Problems) Required
• SciPy solve_ivp Required

Day 13 - 10/30

Homework

• Homework 3 due
• Homework 4 released

Class Material

1. Unit testing [Video]
2. setuptools, packaging [Video]

Link to python-packages repository which is used in the videos.

Reading

• unittest documentation Required at least skim it to see what is in there.
• Introduction to GitHub actions Recommended again, skim to see what is in there
• Python packaging Required

Day 14 - 11/2

Class Material

1. More on Plotting [Video]
2. Basic Interpolation [Video]

Reading

• Mastering SciPy pp. 61 - 104 (Interpolation and Approximation) Required
• SciPy interpolation tutorial Recommended
• PyPlot Use FAQ Required (first half)
• Matplotlib tutorials Recommended take a look around

Day 15 - 11/4

Class Material

1. Intro to RCC [Video by Yian]
2. Integration, Quadrature [Video]

Reading

• RCC User Guide Recommended to familiarize yourself with what is available
• Mastering SciPy pp. 111-123 Required
• scipy.integrate.quad Recommended

Day 16 - 11/6

Homework

• Homework 4 due
• Homework 5 released

Class Material

1. Optimization [Video]

Reading

• Mastering SciPy pp. 125-163 (Optimization) Required OK to skim over code.
• SciPy Optimize Tutorial Recommended

Day 17 - 11/9

Class Material

1. Boundary Value Problems [Video]

Reading

• SciPy integrate.solve_bvp Recommended
• SymPy ode.dsolve Recommended

Day 18 - 11/11

Class Material

1. Pandas [Video]

Reading

• Python for data analysis by Wes McKinney Ch. 5 (Pandas) Required (you can download the chapter or the whole book through the library with a UChicago account)
• Pandas Tutorials Recommended to skim and see if you find something relevant

Day 19 - 11/13

Project

Midterm Checkpoint Due. See guidelines.

Homework

• Homework 6 released

Class Material

1. Scikit Learn [Video]

Reading

• Scikit learn introduction Required
• Scikit learn user guide Recommended Just look through what the possibilities are
• Mastering SciPy pp. 275-309 (Inference and Data Analysis) Recommended - There are some examples with Scikit learn in there.

Day 20 - 11/16

Homework

• Homework 5 due

Class Material

1. Distances [Video]
2. Nearest Neighbor Queries [Video]

Reading

• Mastering SciPy pp. 205-209 (Nearest Neighbors and Range Searching) Required
• Mastering SciPy pp. 179-216 (Computational Geometry) Optional (We’re not going to cover Hulls, Triangulations, and Bezier Curves)

Day 21 - 11/18

Class Material

1. Graphs [Video]
2. NetworkX [Video]

Reading

• Mastering SciPy pp. 199-202 (Shortest Path Problems) Recommended
• NetworkX Tutorial Recommended

Day 22 - 11/20

Homework

• Homework 6 due
• Homework 7 released

Class Material

1. Spectral Graph Theory [Video]
2. Dimensionality Reduction, Plotly [Video]

Reading

• Mastering SciPy pp. 292-298 (Dimensionality Reduction) Required
• Tutorial on Spectral Clustering by Ulrike von Luxburg Optional

Thanksgiving Break

Thanksgiving break is 11/23-11/27

Day 23 - 11/30

Class Material

1. Linear Algebra in PyTorch [Video]
2. Basic Neural Networks in PyTorch [Video]

Reading

• PyTorch Tutorials check them out to see if there is something that matches your interests Optional

Day 24 - 12/2

Class Material

1. Convolutions & FFT [Video]

Reading

• FFT Algorithm by Cooley and Tukey Recommended
• Mastering SciPy pp. 311-374 (Image Processing) Optional

Day 25 - 12/4

Homework

• Homework 7 due

Class Material

1. Scipy stats [Video]

Reading

• SciPy stats tutorial Recommended
• Mastering SciPy pp. 217-274 (Descriptive Statistics) Recommended

Reading Period

Reading Period is 12/5-12/7

Finals Period

Final Project due 12/9. See repository