Colloquium Speaker Tom Griffiths, University of California, Berkeley Friday, January 15, 12:30pm Computer Science 105 Human and machine learning Human cognition still sets the standard we aspire to in many areas of machine learning, including problems such as identifying causal relationships, acquiring and using language, and learning concepts from a small number of examples. In these cases, human and machine learning can establish a mutually beneficial relationship: we can use the formal tools developed in machine learning to provide insights into human learning, and translate those insights into new machine learning systems. I will use the case of causal induction to illustrate the value of this approach, but also highlight some applications in language and concept learning. I will also argue that the same kind of mutually beneficial relationship could potentially exist between developing data-intensive approaches to cognitive science and making sense of large volumes of behavioral data in computer science.