Schedule - Winter 2022

The start of Winter quarter has been delayed 1 week, and the following 2 weeks will be online.

This course follows a Monday/Wednesday 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

System Setup

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

Day 00 - 1/10

Class Material

Lecture Video

Slides for course introduction

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
• Top-10 Algorithms of the 20th Century by B. Cipra. 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

Day 01 - 1/12

Homework

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

Class Material

Lecture Video

1. Functions in Python
2. Asymptotic notation
3. Decorators
4. Recursion

Reading

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

Day 02 - 1/17

MLK Day. No class

Day 03 - 1/19

Homework

• Homework 0 due
• Homework 1 released

Class Material

Lecture Video

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

Reading

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

Day 04 - 1/24

Class Material

Lecture Video

1. Convergence of Algorithms
2. Root Finding
3. Vectorization, numpy ufuncs, numba

Reading

• Newton’s Method on Wolfram Mathworld Recommended
• NumPy Ufuncs Recommended

Day 05 - 1/26

Homework

• Homework 1 due
• Homework 2 released

Class Material

Lecture Video

1. Memory layout
2. Dense Linear Algebra
3. SciPy BLAS and LAPACK Interfaces

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

Day 06 - 1/31

Class Material

Lecture Video

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

Reading

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

Day 07 - 2/2

Homework

• Homework 2 due
• Homework 3 released

Class Material

Lecture Video

1. Agent-based modeling
2. Python Iterators and Generators
3. Sparse Linear Algebra (We’ll start if there is time)

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 08 - 2/7

Class Material

Lecture Video

1. Unit testing
2. setuptools, packaging
3. Sparse Linear Algebra

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

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
• 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 09 - 2/9

Homework

• Homework 3 due
• Homework 4 released

Class Material

Lecture Video

1. Symbolic Computing with SymPy
2. Differentiation
3. Initial Value Problems

Reading

• SymPy Introduction Required Look around the documentation as well.
• Mastering SciPy pp 105 - 110 (Differentiation) Required
• Mastering SciPy pp 165 - 178 (Initial Value Problems) Required
• SciPy solve_ivp Required

Day 10 - 2/14

Class Material

Lecture Video

1. More on Plotting
2. Basic Interpolation
3. 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 11 - 2/16

Homework

• Homework 4 due
• Homework 5 released

Class Material

Lecture Video

1. Intro to RCC
2. Optimization

Reading

• RCC User Guide Recommended to familiarize yourself with what is available
• Mastering SciPy pp. 125-163 (Optimization) Required OK to skim over code.
• SciPy Optimize Tutorial Recommended

Day 12 - 2/21

Class Material

Lecture Video

1. Optimization (continued)
2. Boundary Value Problems
3. Pandas (if time)

Reading

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

Day 13 - 2/23

Homework

• Homework 5 due
• Homework 6 released

Project

Midterm Checkpoint Due. See guidelines.

Class Material

Lecture Video

1. Pandas
2. Scikit Learn

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
• 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 14 - 2/28

Class Material

Lecture Video

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

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.
• 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 15 - 3/2

Homework

• Homework 6 due 3/4
• Homework 7 released 3/4

Class Material

Lecture Video

1. Graphs
2. NetworkX

Reading

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

Day 16 - 3/7

Class Material

Lecture Video

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

Day 17 - 3/9

Homework

• Homework 7 due 3/11

Class Material

1. Linear Algebra in PyTorch
2. Basic Neural Networks in PyTorch

Reading

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

Finals Period

College reading period is 3/12-3/14

Final Project report due 3/18.