Module Code - Title:
CS4242
-
MACHINE LEARNING FOR INTERACTIVE SYSTEMS
Year Last Offered:
2025/6
Hours Per Week:
Grading Type:
N
Prerequisite Modules:
Rationale and Purpose of the Module:
This module provides students with a hands-on opportunity to develop their understanding of machine learning with an emphasis on creative and interactive contexts. Beginning with a review of traditional and also novel input techniques for computational devices and examples from interactive art and game design students will move on to consider the role of machine learning in this context. The central question student will address is how does machine learning enable new and novel modes of interacting with computational systems. The emphasis will be on creative contexts such as digital musical instrument performance and interactive art however the module will reinforce the students general understanding of machine learning and its application.
Syllabus:
Students begin with an introduction to interaction in the context of HCI (human computer interaction) with a focus on interactive art and digital musical instrument performance. The challenges and opportunities when mapping human movement and behaviour to control media in creative contexts are considered. We then explore what is machine learning and different machine learning techniques including classification and regression. Throughout the module students will learn to work with inputs from sensors, sound and video signals, and to design a useful machine learning pipeline for gesture recognition. Students then apply this knowledge in the creation of an interactive system for performance or interaction design.
Learning Outcomes:
Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)
On successful completion of this module, students will be able to:
• Define and explain several key ML algorithms
• demonstrate an understanding of how to effectively choose the most appropriate ML algorithm to employ in a given scenario
• Apply machine learning in the development of a gesture recognition system
• Evaluate efficacy of particular machine learning solutions.
• Use machine learning to enable novel interactions in a performance or human computer interaction context
Affective (Attitudes and Values)
On successful completion of this module, students will be able to:
demonstrate an appreciation the role of Machine learning in creative contexts (interactive art and new media performance)
Critique the value of Machine learning in the creative contexts.
Psychomotor (Physical Skills)
On successful completion of this module, students will be able to:
N/A
How the Module will be Taught and what will be the Learning Experiences of the Students:
The module emphasises hands on learning through practical lab work alongside more theoretical work delivered in lectures. The module will connect with recent developments in the field through the inclusion of guest lectures from practitioners and researchers, and reading material that highlights how machine learning is currently impacting the field of interactive art and digital media performance.
Students learn to collaborate through small group work and to develop their creative ability through open project briefs that call for personal responses to topics.
Research Findings Incorporated in to the Syllabus (If Relevant):
Prime Texts:
Other Relevant Texts:
Programme(s) in which this Module is Offered:
Semester(s) Module is Offered:
Spring
Module Leader:
Malachy.Eaton@ul.ie