CCOG for MUC 250 archive revision 201901
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- Effective Term:
- Winter 2019 through Fall 2021
- Course Number:
- MUC 250
- Course Title:
- AI & Machine Learning in the Arts I
- Credit Hours:
- 4
- Lecture Hours:
- 40
- Lecture/Lab Hours:
- 0
- Lab Hours:
- 0
Course Description
Covers theories and frameworks related to computational or artificial creativity and approaches to endowing machines with creative behaviors. Involves examination of artificial intelligence (AI) and machine learning (ML) in connection with a comprehensive range of arts and creative enterprises such as musical composition and interpretation, sound design, video game creation, drawing, painting, image generation, writing, storytelling, poetry, and design-related tasks. Recommended: MUC 272. Audit available.
Intended Outcomes for the course
Upon completion of the course students should be able to:
- Define artificial intelligence (AI) and machine learning (ML) and key vocabularies of AI/ML to understand and engage in public and academic discourse.
- Recount the technological and social histories of AI/ML, including the technological lineages and surrounding narratives.
- Define computational creativity (artificial creativity or metacreation) and illustrate its historical and contemporary roles with real-world examples in multiple arts (sonic arts, visual arts, literary arts) and from artists of diverse cultural and national identities.
- Demonstrate competence with historical and contemporary AI and ML creative applications, technologies, softwares and/or toolchains.
Course Activities and Design
- Readings
- Lecture
- Videos
- Large and small group dialogue
- Collaborative activities
- Case studies
- Creative content creation using consumer AI and ML applications
Outcome Assessment Strategies
- Writing (short entries and essays). Note: this is not a writing course; students are encouraged to write frequently--as part of department-wide effort to embed communications practice throughout the curriculum--but are not evaluated for competency with Standard American English or academic conventions.
- Creative projects. Students experiment with consumer AI/ML applications, generating a variety of creative content, reflecting on their experiences, and imagining new options (for instance, features they would have liked to see as part of an application).
- Assignments and activities.
- Exams, tests, or quizzes may be used to measure student learning but are not graded.
Course Content (Themes, Concepts, Issues and Skills)
- AI/ML definitions and vocabulary.
- Technological history of AI/ML.
- Narratives of AI/ML--common threads and cultural contexts.
- Computational creativity: case studies
- Historical and contemporary AI/ML applications, softwares, and/or toolchains