S02: Introduction to Python, Colab, and Datasets

UCLA | Cultural Appropriation with Machine Learning

LECTURE SLIDES

COLAB

COURSE DESCRIPTION

This course guides students through state-of-the-art methods for generative content generation in machine learning (ML) with a special focus on developing a critical understanding surrounding its usage in creative practices. We begin by framing our understanding through the critical lens of cultural appropriation. We then extend our understanding into topics such as deep-fakes and bias. Next, we look at how machine learning methods have enabled artists to create digital media of increasingly uncanny realism aided by larger and larger magnitudes of cultural data, leading to new aesthetic practices but also new concerns and difficult questions of authorship, ownership, and ethical usage. Finally we speculate on the future of computational practices, as machine learning becomes an increasingly predominant tool for creatives, and ask of its trajectory, "Where is it all going?".

Shared here are lecture sessions spanning 7 sessions and a guest lecture by the artist and creative technologist Holly Grimm. Students were also engaged through critical assessments of their own work and understanding of the concepts in separate sessions that are not shared here for privacy reasons.

COURSE INSTRUCTOR

LECTURE OUTLINE

S01: Introduction to Cultural Appropriation with Machine Learning
S02: Introduction to Python, Colab, and Datasets
S03: Neural Networks, Feature Extractions, and Manifolds
S04: Searching and Matching
S05: Generative Models for Image Generation
S06: Generative Models for Text Generation
S07: Generative Models for Sound Generation
Guest Lecture: Holly Grimm

LEARNING OUTCOMES

* Understand critical discussions surrounding the usage of machine learning
* Understand what datasets are and how they are created
* Understand how machine learning can be used by artists
* Understand how to generate content within images, text, and audio digital media

CREDITS

This course was presented Fall 2020 at UCLA DMA (Special Topics, DMA171). Special thank you to all the students who continued to challenge and support each other throughout this course. The videos are edited by Katherine Sweetman. RunwayML generously provided Pro accounts/support to students! Zhengyang Huang TA'ed the course. Jonathan Cecil provided additional support as did many wonderful DMA staff/faculty including Lauren McCarthy, Kyle Clausen, and Hope Stutzman.

LICENSE

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Other authors' works may be included during presentations, and additional copyrights may apply.

[vishalahuja]

Can the notebook files be made available please?