For all readings unless otherwise noted, please write down two non “yes or no” questions and type them into the Google doc I’ve shared with you. Be prepared to discuss the questions/readings in class.

Class 10

The Final (due 5/5)

Using what we’ve learned in the class, create a web interface for exploring a dataset. This interface can be portal into a museum collection, a tool that aids in the creative process, or a continuation of your exploration for the midterm.

Metrics for grading:

IMPORTANT - email me a proposal for your final by next week, due 4/21

Class 7

The Midterm (due 3/31), proposal due 3/24

Choose an image data set to explore. This can be an existing API or data set such as a digital museum collection, but it could also be a data set of your own creation such as a website that you scrape or collection of your own photos. Using the tools we covered in the last class, create an analysis of the visual topology of this collection. When using machine learning to look with a macro lens, what patterns emerge? What narratives are newly exposed with these techniques?

Be sure to collect at least 500-600 images (1000 or more would be better) in order to have enough to analyze. The final deliverable for this class will be a series of images, along with a quick in-class presentation. If you are comfortable with web development, please feel free to create something larger for the midterm.

Presenting the project:

If there are no group projects then we will have less than 10 minutes per person to present, “pecha kucha” style. I will be expecting a fairly concrete and succinct presentation of about 5 minutes.

Metrics for grading:

If you are not interested in creating an exploration like this, you can instead build your own set of classifiers for a custom dataset. Using a multivariate dataset of your choosing, try using the KNN, perceptron, and multilayer perceptron algorithms to create different classification models. Present the models to your class and be prepared to discuss what worked, what didn’t work, and which models you found to work best.


Before class, email me:

Class 6 (due 3/10)

Class 5 (due 2/24)

Class 4 (due 2/17)

Class 3 (due 2/10)

Class 2 (due 2/3)

Class 1 (due 1/27)