Schedule

This course follows a T/TH 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 - 9/27

Class Material

1. Basic Bash
2. Install Anaconda Python
3. Install Jupyter notebooks
4. Python Basics

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 - 9/28

Homework

• see the git tutorial if you are not familiar with git version control.

Class Material

1. Bits, Bytes, and Numbers
2. Basic Containers and Packages

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 - 09/30

1. Python Scripts
2. Functions in Python
3. Decorators
4. Modules and Packages

Reading

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

Day 03 - 10/05

1. Python Objects, OOP
2. Asymptotic notation
3. Recursion

Reading

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

Day 04 - 10/07

1. Convergence
2. Root finding
3. Numpy

Reading

• Newton’s method Required
• Memory Recommended

Day 05 - 10/12

Class Material

1. Dense Linear Algebra

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 06 - 10/14

Class Material

1. Sparse matrix formats, scipy.sparse
2. Linear operators

Reading

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

Day 07 - 10/19

Class Material

1. Sparse Linear Algebra

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

Day 08 - 10/21

Class Material

1. Sparse Linear Algebra

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

Day 09 - 10/26

Class Material

1. Agent-based modeling
2. Python Iterators and Generators

   Reading

   • E. Bonabeau Agent-based modeling: Methods and techniques for simulating human systems (2002) Recommended

Day 10 - 10/28

Class Material

1. Symbolic Computing with SymPy
2. Differentiation

Reading

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

Day 11 - 11/02

Class Material

1. Differentiation
2. Initial Value Problems

Reading

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

Day 12 - 11/04

Class Material

1. More on Plotting
2. Basic Interpolation

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 12 - 11/04

Class Material

1. More on Plotting
2. Basic Interpolation

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 13-14 - 11/10-11/12

Class Material

1. Integration, Quadrature
2. Boundary Value Problems

Reading

• Mastering SciPy pp. 111-123 Required
• scipy.integrate.quad Recommended
• SciPy integrate.solve_bvp Recommended
• SymPy ode.dsolve Recommended

Day 15 - 11/16

1. Optimization
2. Convolutions & FFT

Reading

• Mastering SciPy pp. 125-163 (Optimization) Required OK to skim over code.
• SciPy Optimize Tutorial Recommended
• FFT Algorithm by Cooley and Tukey Recommended
• Mastering SciPy pp. 311-374 (Image Processing) Optional

Day 16 - 11/18

1. Graphs
2. NetworkX

   Reading

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

Day 17 - 11/30

1. Spectral Graph Theory
2. Dimensionality Reduction, Plotly

Reading

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