Machine Learning for Design

This technology elective will provide students with the knowledge required to understand, design, and evaluate machine learning systems in the context of the design of intelligent products, services, and systems (iPSSs). Machine learning (ML) is a computational approach that aims at giving computers the ability to learn without being explicitly programmed (A. Samuel, 1959). Smart thermostats, voice-based personal assistants, autonomous vehicles, traffic control systems, online social networks, web shopping platforms, content creation platforms, personal health appliances: much of current and future iPSSs are powered by ML technology, influencing and shaping our interests, habits, lives, and society. To meaningfully envision and design future iPSSs that is beneficial and useful to people and society, designers must: engage with the details of how ML systems see the world, reason about it, and interact with it; experience the quirks, biases, and failures of ML technology; contend with how agency, initiative, trust, and explainability mediate the interaction between human and iPSSs; and understand how functionalities enabled by ML can be designed in iPSSs. Students in this course will gain practical experience with ML technology and learn how to think critically of what ML systems can do, and how they could and should be integrated into iPSSs.

The course will cover the following topics:

1. AI and ML in intelligent products, services, and systems (design). The students will be introduced to the role that Artificial Intelligence and, more specifically, Machine Learning technology play in iPSSs, in their design process, and in their lifecycle. The most important ML concepts, terminology, paradigms, and methods will also be described and exemplified.

2. Text Processing methods for iPSSs. Building on the knowledge acquired in the DATA course, students will be introduced to existing ML techniques for text processing (parsing, analysis, generation), and will practice with their use in realistic design contexts.

3. Image Processing methods for iPSSs. Students will be introduced to existing ML techniques for image processing (acquisition, analysis, generation), and will practice with their use in realistic design contexts.

4. Creating Machine Learning Models Students. will deepen their knowledge in how machine learning models could be trained, and on how their performance could be evaluated. Specific attention will be devoted to the task of training data a) design, b) creation, and c) usage in realistic adoption scenarios.

5. Designing iPSSs that include Machine Learning technology. In this final module, students will learn how to design (using human-centred approaches) intelligent products, services, and systems. Students will understand which issues could impact the experience of people when interacting with iPSSs, and learn to assess how ML technology can and should be integrated into iPSSs.