Assignments
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.
Week 12
The Final (due 12/13)
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:
- 25% - Originality of concept - Does the project explore an original and novel concept?
- 25% - Fit and finish - Is the project finished? Was there clearly thought put into this?
- 25% - Theme - Did the project follow the prompt?
- 25% - Presentation - Is the presentation coherent and thoughtful?
Week 10 (due 11/8)
Week 7
The Midterm (due 10/25)
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 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:
- 25% - Originality of concept - Does the project explore an original and novel concept?
- 25% - Fit and finish - Is the project finished? Was there clearly thought put into this?
- 25% - Theme - Did the project follow the prompt?
- 25% - Presentation - Is the presentation coherent and thoughtful?
Due 10/18:
Before class, email me:
- One paragraph describing what your project explore
- If applicable, a link to the data set you will be using
- The name of the data set you will be using
Week 6 (due 10/11)
-
Read
- A New Approach to Understanding How Machines Think - TCAV concept is less important than surrounding context
- Dimensionality-Reduction Algorithms for Progressive Visual Analytics - just summary and introduction
-
Code
- Try using our multilayer perceptron with a different dataset. Can you get it to work with CIFAR-10 or Fashion MNIST datasets? What about the Iris dataset? Try playing with hyperparameters and number of layers/connections. I highly recommend reviewing the referenced material in the class notes if you are at all confused.
Week 5 (due 10/3)
- Read
- Code
- Continue trying to scrape websites using what we covered in class.
Week 4 (due 9/27)
- Read
- Using Artificial Intelligence to Augment Human Intelligence
- Creative AI - optional reading, will discuss in class
- Code
- Review the notes from class and be sure you understand the concept of the perceptron
- Watch videos/read articles if unclear
- Try implementing the perceptron on another dataset
Week 3 (due 9/20)
- Read
- Watch (optional)
- Code
- Review the in class jupyter notebook
- Try implementing your own classifier using one of the other Scikit learn toy datasets. Google around for ideas. This will not be graded or collected.
Week 2 (due 9/13)
- Read
- The Myth of AI by Jaron Lanier (just read article not comments or video)
- More States Opting To ‘Robo-Grade’ Student Essays By Computer
- Don’t Call AI “Magic” by M.C. Elish
- Code
- Review the in class jupyter notebook
- If you are having a hard time with Python, try doing all the “Learn the Basics” section at learnpython.org
- I recommend also picking up Learn Python 3 the Hard Way if you like learning from printed books