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Schedule - Fall 2023

This course follows a Tuesday/Thursday schedule. There is a section for each day, with materials for that day. This schedule is subject to change before a class is held.

Schedule Archives: Fall 2020 Fall 2021 Winter 2022

System Setup

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

Day 00 - 9/26

Class Material

1. Python Basics
2. Bits, Bytes, and Numbers
3. Basic Containers and Packages
4. Python Scripts [Example Script] [Download Example]

Reading

• Discrete or Continuous? by N. Trefethen. Required
• 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
• Top-10 Algorithms of the 20th Century by B. Cipra. Recommended for those interested in the culture of scientific computing.

Day 01 - 9/28

Homework

• Homework 0 released.

Day 02 - 10/3

Class Material

1. Decorators
2. Vectorization, numpy ufuncs, numba
3. Memory layout

Reading

• NumPy Ufuncs Recommended

Day 03 - 10/5

Class Material

1. Dense Linear Algebra
2. SciPy BLAS and LAPACK Interfaces
3. Python Objects, OOP 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 13 - 18 (Creation of matrices) required
• 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

• LAPACK on netlib Optional

• BLAS on netlib Optional

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

Day 04 - 10/10

Homework

• Homework 2 released

Class Material

1. Modules and Packages [GitHub repository]
2. Convergence of Algorithms
3. Root Finding

Reading

• Modules Required
• Newton’s Method on Wolfram Mathworld Recommended

Day 05 - 10/12

Homework

• Homework 1 due

Class Material

1. Linear operators
2. Sparse matrix formats, scipy.sparse
3. Sparse Linear Algebra (We’ll start if there is time)

Reading

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

Day 06 - 10/17

Class material

1. Sparse Linear Algebra (Continued)
2. Differentiation

Reading

• Mastering SciPy pp 105 - 110 (Differentiation) Required
• SciPy solve_ivp Required

Day 07 - 10/19

Homework

• Homework 2 due
• Homework 3 released
• Project groups finalized

Class material

1. Initial Value Problems
2. Unit testing
3. More on Plotting
4. Sympy

Reading

• unittest documentation Required at least skim it to see what is in there.
• Mastering SciPy pp 165 - 178 (Initial Value Problems) Required
• Matplotlib tutorials Recommended take a look around

Day 08 - 10/24

Class material

1. Interpolation
2. Integration, Quadrature

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
• Mastering SciPy pp. 111-123 Required
• scipy.integrate.quad Recommended

Day 09 - 10/26

Homework

• Homework 3 due
• Homework 4 released

Class material

1. Condition numbers
2. Python Iterators and Generators

Reading

• Python Tutorial on Iterators Required

Day 10 - 10/31

Homework

• Project proposal due

Class material

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

Reading

• Python Tutorial on Generators Required

Day 11 - 11/02

Homework

• Homework 4 due
• Homework 5 released

Class Material

1. Optimization

Day 12 - 11/07

Class Material

1. Pandas
2. Scikit Learn

Day 13 - 11/09

Homework

• Homework 5 due
• Homework 6 released

Class Material

Cancelled

Day 14 - 11/14

Homework

• Midterm checkpoint due 11/14

Class Material

1. Distances
2. Nearest Neighbor Queries
3. Dimensionality Reduction, Plotly
4. Linear Algebra in PyTorch

Day 15 - 11/16

Homework

• Homework 6 due
• Homework 7 released

Class Material

1. Basic Neural Networks in PyTorch
2. Scipy distributions
3. Monte Carlo Methods

Day 16 - 11/28

Class Material

1. Fast Multipole Method

Day 17 - 11/30

Homework

• Homework 7 due

Class Material

1. Fast Fourier Transform (FFT)
2. Boundary Value Problems

Project

Groups finalized 10/19.

Project proposal due 10/31.

Midterm checkpoint due 11/14.

Final Project report due 12/7.

Finals Period

College reading period is 12/2-12/4