CSCI 385 - Scientific Computing

Course Details

Recommended Textbooks

A Primer on Scientific Programming with Python by Hans Petter Langtangen, 2012 Find this text on Amazon

Introduction to Computational Science by Angela B. Shiflet and George W. Shiflet, 2014 Find this text on Amazon

This class is BYOL. Bring a laptop computer to class with you every day, unless there is a test. I have a small number of computers available for students who are unable to do so. We will do programming every class period.

In particular we will be using IPython, primarily with the SciPy, NumPy and MatPlotLib packages. The best way to install these is to use Anaconda for Python 2.7. This is available for Windows, Mac and Linux.

Overview

Students study problems arising from the physical, biological, and/or social sciences and the algorithms and theory used to solve them computationally. Included among the problems are numerical methods for maximizing a function and solving a differential equation. Prerequisite: MATH 130 and CSCI 150.

Learning Objectives

By the end of this course, among other things you should be able to:

• visualize data from a wide variety of sources,
• analyze data using exploratory data analysis and clustering techniques
• understand the basic tools for curve-fitting and interpolation,
• approximate the roots of a continuous function by several techniques and understand the strengths and limitations of these techniques,
• understand the important issues of numerical computations with matrices (systems of linear equations and inequalities) and detect when serious issues will arise when computing with them,
• understand and perform basic techniques in signal processing,
• build and solve system dynamics problems, and
• construct a Monte-Carlo simulation model.

Disabilities

It is the policy of Hendrix College to accommodate students with disabilities, pursuant to federal and state law. Students should contact Julie Brown in the Office of Academic Success (505.2954; brownj@hendrix.edu) to begin the accommodation process. Any student seeking accommodation in relation to a recognized disability should inform the instructor at the beginning of the course.

Academic Honor

As stated in the Hendrix Academic Integrity Policy, all students have agreed to adhere to the following principles:

• All students have an equal right to their opinions and to receive constructive criticism.
• Students should positively engage the course material and encourage their classmates to do the same.
• No students should gain an unfair advantage or violate their peers' commitment to honest work and genuine effort. It follows that any work that a student submits for class will be that student's own work. The amount of cooperation undertaken with other students, the consistency and accuracy of work, and the test-taking procedure should adhere to those guidelines that the instructor provides.
• Members of the Hendrix community value and uphold academic integrity because we recognize that scholarly pursuits are aimed at increasing the shared body of knowledge and that the full disclosure of sources is the most effective way to ensure accountability to both ourselves and our colleagues.

I will provide guidelines for the amount of cooperation allowed on each assignment as they are posted.

Extensions

Extensions and rescheduling for labs, projects, exams and quizzes are only given when circumstances beyond your control (e.g. being sick, academic, choir or sports travel, etc) prevent you from completing a project on time. You must notify me either by email or phone of your circumstances well in advance of the due date. No extensions are given for requests made within three days of the due date.

Quizzes and Participation

You should attend class and participate in discussions every day, answering questions, asking questions, presenting material, etc. Also, sporadically throughout the semester, there will be short quizzes covering material from the previous class. Active participation will comprise 5% of your final grade.

Syllabus

We will be using textbook and additional supplemental material such as relevant web-pages and background material for the course. Readings will be assigned before material will be covered in class. You are expected to review the material and come to class prepared. As readings are assigned, they will be posted here.

Scratch Pad

• Intro Lecture
  E. Coli Genome
  Counting IPython Notebook
  Tips for Faster Python

• matplotlib
  Linear Regression
  tips.csv
  TipAnalysis IPython Notebook
  

• Weather Underground
  ZipLocate
  ZIPExpress
  Drawing Circles in Matplotlib
  Matplotlib Colors
  WeatherUnderground IPython Notebook
  CircleDraw IPython Notebook
  

• Matplotlib in Paths
  USA Shape Files (Thanks to Kevin Wayne)
  XKCD Map Projections
  Choropleth Maps
  ZipMap IPython Notebook
  

• ClusteringKMeans IPython Notebook
  ClusterPartII.ipynb IPython Notebook
  K-Means Clustering Example
  K-Means Color Reduction
  Scipy K-means and Vector Quantization
  Principal Component Analysis
  PCA Example
  PCA IPython Example
  3D Plots in matplotlib
  Interpretation of Principal Components
  

• Euler's Method
  Logistic Growth
  Newton's Law of Cooling
  Estimating Time of Death
  Populations and Cooling IPython Notebook
  

• Compartmental Models in Epidemiology
  Incubation Period
  Epidemic Model
  Basic reproduction number
  Epidemiology and Fear
  SIR Basic Model IPython Notebook
  

• Lorenz System
  Biological Neuron Models
  Hindmarsh-Rose Model
  System Dynamics IPython Notebook
  odeint IPython function
  

• Runge Kutta Methods
  Runge Kutta Lab
  Runge Kutta from Mathworld
  EulerAndRungeKutta IPython Notebook
  

• Pseudo Random Number Generators
  RANDU
  Mersenne Twister
  Random numbers for sale
  Diehard Tests of Randomness
  Random Numbers IPython Notebook
  

• Monte Carlo Integration
  Random Walk
  Monte Carlo IPython Notebook
  

• Markov chains
  Examples of Markov chains
  Markov Chain IPython Notebook
  

• Log Probabilities
  Hidden Markov Model
  Notes on HMMs
  

• Cellular Automaton
  Game of Life
  Game of Life IPython Notebook
  

• Wildfire Safety
  Forest Density
  Density Management of Sierra Forests
  Wildland Fire spread modeling with cellular automata
  Forest Fire Model
  Forest Fires IPython Notebook
  

• Heat Diffusion
  Molecular Diffusion
  Diffusion IPython Notebook
  

• Agent-Based Models
  Trail pheromones
  Ant Colony IPython Notebook
  

• Wolves and Sheep IPython Notebook
  Wolves and Sheep and Grass IPython Notebook
  

• Local Optimization
  Evolutionary Algorithms for TSP
  

Labs

Much of your experience with modeling in this course will be through semi-weekly labs, which will comprise 40% of your final grade. All labs are weighted individually within the Lab portion of your final grade.

• Lab 0: Who Are You?
• Lab 1: The Python Challenge
• Lab 2: FEV1 and Lung Volume
• Lab 3: Choropleths and GPS/API Mashups
• Lab 4: K-means Clustering and PCA
• Lab 5: Caffeination
• Lab 6: Monte Carlo Simulation
• Lab 7: Markov Models
• Lab 8: Cells and Agents
• Lab 9: Optimization

Final Project

You will be working on a summative final project for the course, applying at least two approaches learned in class to a real-world modeling problem. This will comprise 25% of your final grade.

Exams

There will be 2 exams, each worth 15% of your final grade.

• Exam 1: Oct 10

• Exam 2: Nov 19

Grading

Your final grade for this course will be based on the Labs, Projects, Quizzes, Exams and Participation described above.

Grading Scale 90-100 A 80-89 B 70-79 C 60-69 D 0-59 F

Weights Labs 40% Final Project 25% Participation 5% Exam 1 15% Exam 2 15%

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© Mark Goadrich, Hendrix College