Watch a recoring of the full class here on YouTube
Curriculum for the class at https://github.com/handav/curriculum/blob/master/SubjectiveData.md
Slides for class at https://drive.google.com/open?id=117x4LZx_OxD65nuQVGpLtGoi-kP-lkDY. These slides are available with attribution only.
This session looked at emotional/subjective data and dataset creation. What does ‘subjective data’ means, how to capture abstractions like emotions and other subjective experiences in datasets and examine currently available emotional datasets, exploring them critically.
Participants will learn to identify subjectivity in “objective” datasets by looking at how the datasets were created, who tagged them, who funded the dataset and what the dataset leaves out. We will explore “artisanal data”, creating explicitly subjective datasets for artistic purposes and talk about how a culture’s values can be a type of bias. This workshop will explore how to avoid bias retention over time including experiments with labels and vocabulary – how we can create these, and use them with regularity. Finally, participants will create and explore their own subjective dataset with a focus on how to be cognizant during the collection, analysis, and usage stages.
- Hannah Davis is a programmer and generative musician from NYC. Her work falls along the lines of music generation, data sonification, natural language processing, and sentiment analysis. Her algorithm TransProse, which translates novels and other large works of text into music, has been written up in TIME, Popular Science, Wired, and others. A human-computer collaboration, where she analyzed the sentiment of articles talking about technology over time, was performed by an orchestra at The Louvre this past fall. Hannah is currently working on creating unique datasets for art and machine learning, and is also working on a project to generativly score films. She is a 2017 AI Grant recipient. see also musicfromtext.com
Curriculum from the class at https://github.com/yining1023/curriculum/blob/master/FriendlyMachineLearning.md
Slides for the class at https://docs.google.com/presentation/d/1gM8F5Ybo4z27q3kGWc2cw-fQ3x5Wf-Zj71InlC091z8/edit?usp=sharing
This workshop focused on doing deep learning in the browser with javascript. We will use ML5, an experimental library that does not require any setup tools or complicated frameworks to install. This will be a beginners friendly workshop and no previous knowledge of machine learning is required. Basic experience with javascript would be helpful.
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Cristóbal Valenzuela is a technologist and software developer interested in building digital tools and interactive experiences. His work has been sponsored by Google and the Processing Foundation. He is currently a M.P.S Candidates at the Interactive Telecommunications Program (ITP).
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Yining Shi is a creative technologist and a software engineer. Her research interests lie in developing novel ways of learning and teaching computational topics through various media like machine learning, creative coding, physical computing, and data visualization. Yining is also a contributor to various open source projects from the Processing Foundation and New York University’s Interactive Telecommunications Program.
- Lauren G @poohlaga
- Marcha @majjism
- Kyle @foxportal
- Ayo @ayoio
- April @aprilsotarman
- Lauren B @lalabarnett
- Garrett @brks_grrtt
- Brienne @b_of_j
- Chris @crissian
- Alyssa @doublegemz
- Savannah @sbadalich
- Tracy @wagongrrl
- Nancy @lesheepo
- April @aprilsotarman
- Lauren B @lalabarnett
- Peter @prayweto
- Amminder @amminder
- Hannah @ahandvanish
- Emma @doodybrains
- Angelina @oxangiemarie
- Katherine @kakirine
- Antonia @antoniablairart