Course Overview
The problem of human intelligence — its nature, how it is produced by the brain and how it could be replicated in machines — is a deep and fundamental problem that cuts across multiple scientific disciplines. Philosophers have studied intelligence for centuries, but it is only in the last several decades that developments in science and engineering have made questions such as these approachable: How does the mind process sensory information to produce intelligent behavior, and how can we design intelligent computer algorithms that behave similarly? What is the structure and form of human knowledge — how is it stored, represented, and organized? How do human minds arise through processes of evolution, development, and learning? How are the domains of language, perception, social cognition, planning, and motor control combined and integrated? Are there common principles of learning, prediction, decision, or planning that span across these domains?
This class provides instruction on the mechanistic basis of intelligence, with the focus on answering the question of how does the brain produce intelligent behavior. We will focus on a quantitative computational approach, combining experimental techniques in neuroscience and cognitive science with computational modeling in order to elucidate the computational architecture of human intelligence.
Through lectures by the various members of the Center for Brains, Minds, and Machines, the course will explore recent progress in building and understanding a representation of the environment, which is rich enough to allow us to act on the world around us and to react to events that take place in it. Such a representation enables and reflects computations that detect objects and their interactions, interpret distances, relative order and movement; it includes planning of saccades, navigation, grasping, and abstract scene understanding. The lectures include empirical studies in humans and primates using psychophysical, imaging, and physiological tools.
The format of the class is different from the last two years. The class is not any longer project based; it is reverting to the traditional format of lectures and recitations. Instead of an exam, small teams will have to complete a project, chosen earlier, during the last two weeks of the class. Students are expected to conclude the class with an oral presentation of their project. The class is also a great opportunity for students who plan to apply to graduate school or pursue a career in industry research, since it is important in both cases to be able to communicate research effectively both orally and in writing. The end-of-class projects should relate computational and empirical findings on a topic that was presented during the course. Students will choose a project from a list that will be distributed in mid-october. Instruction and practice in oral and written communication will be provided.
Prerequisites
6.036 and (9.40 or 18.06), or permission of instructor
Class Meetings
Lectures W 130-330 (In Person in Room 46-3189)
Recitation: M 2-3 (In Person in Room 46-3310)
All classes held in Person unless otherwise instructed
First class meets on Wednesday, September 8th, 2021.
Grading [Updated September 10th, 2021]
20% : Attendance + Participation
15% : Written Proposal
15%: Oral Proposal Presentation
25%: Final Written Assignment
25%: Final Oral Presentation