2004 Fall
9.66/9.914
Computational Cognitive Science
This course is an introduction to computational theories of human
cognition. Drawing on formal models from classic and contemporary
artificial intelligence, we will explore fundamental issues in human
knowledge representation, inductive learning and reasoning. Readings
will include seminal and
state-of-the-art research papers from the cognitive, AI, and machine
learning literatures, as well as textbook chapters and tutorials on
technical approaches.
My final paper.
2004 Spring:
MAS.630
Affective Computing
Explores computing that relates to, arises from, or deliberately
influences emotion. Topics include the interaction of emotion with
cognition and
perception, the role of emotion in human-computer interaction, the
communication of human emotion via face, voice, physiology, and
behavior, construction of computers that can recognize and respond
appropriately to human emotional expressions, the development of
computers that "have" emotion, and other areas of current research
interest.
My final project.
MAS.966
/ 15.970 Digital Anthropology
Digital Anthropology is an applied social
science and media arts seminar, surveying the blossoming
arena of digital-artifact enabled experimental
sociology/anthropology. We will emphasize on both (a)
Technology
Testbeds – systematically deploying research lab prototypes and
corporate pre-production products in a sample human organizational
population and carefully observing the social consequences, and (b)
Sociometrics – using digital artifacts to better observe and measure
the complex social reality of interesting human systems.
15.838 Research Seminar in Marketing
Seminar on current marketing literature and current research interests
of faculty and students. Topics such as marketing models, consumer
behavior, competitive strategy, marketing experimentation, and game
theory. Restricted to doctoral students.
2003 Fall:
6.867 Machine
Learning
This introductory course on machine learning will give an overview
of many concepts, techniques, and algorithms in machine learning,
beginning with topics such as linear regression and ending up with
more recent topics such as boosting, support vector machines, hidden
Markov models, and Bayesian networks. The course will give the student
the basic ideas and intuition behind modern machine learning methods
as well as a bit more formal understanding of how, why, and when they
work. The underlying theme in the course is statistical inference as
it provides the foundation for most of the methods covered.
Psychology
1501 (Harvard)
Surveys interpersonal and group processes in
organizational settings.
Includes how groups and organizations affect individual members and
vice versa; interpersonal and group processes; work team behavior and
performance; power dynamics in organizations; intergroup relations; the
leadership of groups and organizations.
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