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Schedule - Fall 2025

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 Fall 2023 Fall 2024

System Setup

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

Day 00 - 09/30

Class Material

Intro Slides

1. Python Scripts [Example Script] [Download Example]

2. Python Basics

3. Basic Containers and Packages

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 - 10/02

Homework

• Homework 0 released.
• See the git tutorial if you are not familiar with git version control.

Class Material

1. Functions in Python
2. Recursion
3. Bits, Bytes, and Numbers
4. Asymptotic notation

Reading

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

Day 02 - 10/07

Class Material

1. Decorators
2. Vectorization, numpy ufuncs, numba
3. Memory layout
4. Python Objects, OOP

Reading

• NumPy Ufuncs Recommended

Day 03 - 10/09

Homework

• Homework 0 due.
• Homework 1 released.

Class Material

1. Python Objects, OOP (continued)
2. 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 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 - 10/14

Class Material

1. Dense Linear Algebra (Continued)
2. SciPy BLAS and LAPACK Interfaces
3. Modules and Packages [GitHub repository
4. Convergence of Algorithms
5. Root Finding

Reading

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

Day 05 - 10/16

Class Material

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

Reading

• Mastering SciPy pp 19 - 28 (Creation of sparse matrices, linear operators) Required
• Mastering SciPy pp 105 - 110 (Differentiation) Required

Day 06 - 10/21

Class material

1. Sparse Linear Algebra (Continued)
2. Differentiation

Reading

• Mastering SciPy pp 105 - 110 (Differentiation) Required

Day 07 - 10/23

Homework

• Homework 2 due
• Homework 3 released

Class material

1. Differentiation (Continued
2. Initial Value Problems
3. Unit testing
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
• SciPy solve_ivp Required

Day 08 - 10/28

Class material

1. More on Plotting
2. 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
• Mastering SciPy pp. 111-123 Required
• scipy.integrate.quad Recommended

Day 09 - 10/30

Homework

• Homework 3 due
• Homework 4 released

Class material

1. Integration, Quadrature
2. Python Iterators and Generators

Reading

• Python Tutorial on Iterators Required
• Python Tutorial on Generators Required

Day 10 - 11/04

Homework

• Project proposal due

Class material

1. Condition numbers
2. Agent-based modeling

Day 11 - 11/06

Homework

• Homework 4 due
• Homework 5 released

Class material

1. Optimization
2. Pandas

Day 12 - 11/11

1. Scikit Learn
2. Distances
3. Nearest Neighbor Queries

Day 13 - 11/13

Homework

• Homework 5 due
• Homework 6 released

Class Material

1. Nearest Neighbor Queries (continued)
2. Dimensionality Reduction, Plotly
3. Linear Algebra in PyTorch
4. Basic Neural Networks in PyTorch
5. Scipy distributions

Day 14 - 11/18

No class

Day 15 - 11/20

Homework

• Homework 6 due
• Homework 7 released
• Project checkpoint due

Class material

1. Monte Carlo Methods
2. Introduction to Fourier methods

Day 16 - 12/05

Homework

• Homework 7 due
• Homework review

Class material

1. Introduction to Fourier methods
2. Boundary Value Problems

Project

Groups finalized 10/20.

Project proposal due 11/4.

Midterm checkpoint due 11/19.

Final Project report due 12/11.

Finals Period

College reading period is 12/6-12/8