Samples of student work
Thanks to all of my fantastic students and our inspiring guest speakers this semester. Some of these students created videos of their work, a few of which I share below. A more formal description of the course is below the videos.
Many applications of machine learning involve interactions with humans. Humans may provide input to a learning algorithm, including input in the form of labels, demonstrations, corrections, rankings, or evaluations. And they could give such input while observing the algorithm’s outputs, potentially in the form of feedback, predictions, or demonstrations. Although humans are an integral part of the learning process, traditional machine learning systems used in these applications are agnostic to the fact that inputs/outputs are from/for humans. However, a growing community of researchers at the intersection of machine learning and human-computer interaction are making interaction with humans a central part of developing machine learning systems. These efforts include applying interaction design principles to machine learning systems, using human-subject testing to evaluate machine learning systems and inspire new methods, and changing the input and output channels of machine learning systems to better leverage human capabilities. This course focuses on interactive machine learning (IML), which I define to be machine learning with a human in the learning loop, observing the result of learning and providing input meant to improve the learning outcome.
In this research-focused course, we will explore the motivation for interactive machine learning, create and analyze a range of simple interactive machine learning systems, and cover a breadth of specific interactive machine learning problems and approaches. Much of the course will be focused on building towards and executing a final research project that involves a machine learning algorithm with a human in the loop.
The course will consist of small hands-on projects and a final research project; guest lectures by researchers of interactive machine learning and related areas; student-written reviews and discussions on readings, some of which the class will choose; flipped classroom content and lecture when necessary; student presentations on explorations of available toolkits for machine learning, visualization, etc..
• A familiarity with machine learning. We will review many machine learning concepts, but this course is unlikely to teach the fundamentals of machine learning to someone who is unacquainted. Completing a MOOC course (e.g., on Coursera or EdX) on machine learning would provide sufficient background.
• Strong programming skills
• Saleema Amershi (Microsoft Research)
• Rebecca Fiebrink (Princeton)
• Krzysztof Gajos (Harvard)
• Nick Gillian (MIT)
• Kshitij Judah (Oregon State)
• Joe Konstan (Univ. of Minnesota)
• Tessa Lau (Savioke/formerly Willow Garage)
• Henry Lieberman (MIT)
• Kayur Patel (Google/Columbia)
• Simone Stumpf (City University London)