DMA 100: Selected Topics In Arts - Spring 2017

COURSE TITLE:

Find By Image: Machine Learning for Artists

Tues and Thurs 2-4:50pm
Office Hours: Tues 12-1pm

INSTRUCTOR:

Luke Fischbeck lfischbeck@gmail.com

TEACHING ASSISTANT(S):

Lee Tusman


DESCRIPTION:

This studio-based course aims to introduce machine learning—a complex and quickly evolving field—to artists, designers and performers.The goal of this course will be to unpack and familiarize ourselves with available machine learning tools, which we will put to use in planning and producing works of art. In-class labs will open a preliminary investigation into the conceptual and technical underpinnings of key machine learning methods, exploring their application through hands-on demonstrations. Readings and discussions will attempt to connect the theory, practice, and poetics of machine learning, and to place our efforts into a wider art-historical context. In the process, we will expand on the general phenomena of learning, experience, and creativity as subject matters in themselves.

download / view syllabus as PDF


RESOURCES



creative work / research:


creativeAI
altAI conference
openAI
wavenet
google magenta
research.fb.com
fast forward labs


libraries / packages:


deep learning docker container
tensorflow
torch
theano
scikit-learn
deeplearning4j
keras
keras.js
weka
wekinator
moa


cloud platforms:


google ML engine
aws ML
bigML
floydhub
orange


projects:


pix2pix
openface
convnet.js
recurrent.js
torch-rnn
tensorflow-wavenet
interactive-abstract-pattern-generation-js
videogan
tensorflow/models/tree/master/syntaxnet
audio-texture-synthesis-and-style-transfer
neural-doodle
brain.js
RNN-artist
StackGAN
dcgan
fairML
merlin speech synthesis
people.eecs.berkeley.edu/~junyanz


courses:


ml4a.github.io
kadenze.com/courses/machine-learning-for-musicians-and-artists
cs229.stanford.edu
itp.nyu.edu/varwiki/Syllabus/LearningBitbyBitS10
coursera.org/learn/machine-learning
stanford.edu/class/cs224n
patrickhebron.com/learning-machines
kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow
udacity.com/course/intro-to-machine-learning--ud120
udacity.com/course/deep-learning--ud730
web.cs.ucla.edu/~sriram/courses/cs188
ciml.info
Courses/UCLA/Stat_231
machine-learning-is-fun


lists of resources:


deep-learning
tensorflow-github-projects
machine-learning
Machine-Learning-Tutorials
kaggle.com/
kdnuggets.com/


technical reading (whitepapers):


gitxiv.com/
most cited: deep-learning-papers
roadmap: Deep-Learning-Papers-Reading-Roadmap
ML ethics research: www.fatml.org


theory / context / reading:


turing
a-sea-of-data-apophenia-and-pattern-mis-recognition
abnormal-encephalization-in-the-age-of-machine-learning
machine-bias-risk-assessments-in-criminal-sentencing
artificial-intelligences-white-guy-problem


textbooks:


deeplearningbook.org


datasets:


MNIST
imageNet
COCO
homepages.inf.ed.ac.uk/jyamagis
CIFAR
statmt.org/europarl
kaggle.com/datasets
cstr.ed.ac.uk/downloads/
100-best-free-data-sources
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