CME 193 - Introduction to Scientific Python

Project

The project is meant for you to demonstrate the skills you have learnt by taking this class. You are free in the choice of your project. However, picking the right project can be hard, so it might a safe option to pick one of the project ideas listed below.

The goal of the project should be to have fun. You take this class because you want to learn about Python, and this is your opportunity to use Python in any way you like. The project should take you around 20 hours of work as a rough estimate.

Delivarables and deadlines

• Project proposal, due at the beginning of Lecture 4, on April 23. A brief write-up should describe what you intend to do. This document should be a pdf file, and should be about two paragraphs long.

• Final version of Python scripts, due one week after Lecture 8, on May 28 at noon. The final version should include all source code and data, and be zipped. Make sure your code runs (whatever the input) smoothly.

• Final write-up, due one week after Lecture 8, at noon An updated version of your write-up, explaining things that have changed and your results.

There are no late days and no exceptions. You are to work on the project on your own, though you are strongly encouraged to discuss your project and code with others.

Please turn in all your work using the Dropbox on Coursework.

Project ideas

Below you can find some project ideas, to give you a sense of what would be possible and the scope of the project. Some of the below might be a bit ambitious, but I think it is good to be ambitious and end up not implementing everything completely.

Of course, you are very welcome to do something very different. Let the following list then give you an idea of the scope of your project.

Neural networks

Recently, neural networks and deep learning have become very fashionable. You could either explore the existing packages and work on a dataset, or implement a neural network trainer yourself, which should include training and prediction. Of course your own implementation should not be as sophisticated as the ones in some of the packages, but it will give you a good understanding of neural networks.

References

• A tutorial on deep learning

• Blogpost about implementing a convolutional neural network

• PyBrain, a neural network library for Python Also check out the source code on github.

Movie recommendation system

The MovieLens 100k dataset holds information on a reasonably large set of movies and user ratings. Implement a recommendation system that predicts movie ratings for users, finds similar movies and users, etc.

Simulation of Uber and Lyft

Uber and Lyft are companies that connect cab drivers to customers. Implement a simulation system where drivers roam around in a world (a 2D grid) and customers pop up dynamically, are assigned to drivers, and then brought to their destination. Visualize the simulation, for example using Matplotlib.

College football stats

Colege football stats has comprehensive datasets on the last 9 years of college football. There are plenty of ways to be creative, one option would be to find a new model to predict outcomes of games or rank teams. Start simple and improve your model as you add more of the data into your analysis.

Enigma machine

The Enigma machine is a well known device to encrypt messages, primarily used by the Germans in the second world war and famously cracked by the Allied forces. It would be interesting to create a Python version of the Enigma machine, that can encrypt and decrypt messages. You can find plenty of information online about the Enigma machine. Of course, you could also implement more contemporary encryption methods.

Dynimical systems and predator-prey models

Predator-prey models, such as the Lotka-Volterra equations, describe the dynamics of biological systems where two species interact. Using Python, one is able to simulate much more complicated biological systems, with multiple species and additional factors (e.g. location, proximity to water, etc.). Postulate the features of a dynamical system, implement a simulation and present results graphically.

2048 Game

The game 2048 is a simple yet addictive game. Write the game in Python, such that you can play it in your terminal. Additionaly, write a computer agent that plays the game automatically and does reasonably well by coming up with some clever heuristics.