This course explores the problem of intelligence—its nature, how it is produced by the brain and how it could be replicated in machines—using an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines. Materials are drawn from the {{% resource_link "450d58d6-39f7-4ace-a86a-23d99f7abd76" "Brains, Minds and Machines Summer Course" %}} offered annually at the Marine Biological Laboratory in Woods Hole, MA, taught by faculty affiliated with the {{% resource_link "a3b472c1-b2b2-48e7-ac79-5166d50623f1" "Center for Brains, Minds and Machines" %}} headquartered at MIT. Elements of the summer course are integrated into the MIT course, _9.523 Aspects of a Computational Theory of Intelligence._ Contributors ------------ This course includes the contributions of many instructors, guest speakers, and a team of iCub researchers. See the [complete list of contributors](/courses/res-9-003-brains-minds-and-machines-summer-course-summer-2015/pages/syllabus/course-instructors-guest-speakers-and-icub-team).

Syllabus

Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, & Machines
Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience
Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback
Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1
Lecture 1.4: James DiCarlo - Neural Mechanisms of Recognition Part 2
Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence
Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1
Lecture 1.7: Matt Wilson - Hippocampus, Memory, & Sleep Part 2
Seminar 1: Larry Abbott - Mind in the Fly Brain
Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1
Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2
Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3
Lecture 3.1: Liz Spelke - Cognition in Infancy Part 1
Lecture 3.2: Liz Spelke - Cognition in Infancy Part 2
Lecture 3.3: Alia Martin - Developing an Understanding of Communication
Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information
Lecture 3.5: Josh Tenenbaum - The Child as Scientist
Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit
Lecture 4.1: Shimon Ullman - Development of Visual Concepts
Lecture 4.2: Shimon Ullman - Atoms of Recognition
Lecture 4.3: Aude Oliva - Predicting Visual Memory
Seminar 4.1: Eero Simoncelli - Probing Sensory Representations
Seminar 4.2: Amnon Shashua - Applications of Vision
Lecture 5.1: Boris Katz - Vision and Language
Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again
Lecture 5.3: Patrick Winston - Story Understanding
Seminar 5: Tom Mitchell - Neural Representations of Language
Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence
Lecture 6.2: Ken Nakayama - The Social Mind
Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1
Lecture 6.4: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 2
Lecture 7.1: Josh McDermott - Introduction to Audition Part 1
Lecture 7.2: Josh McDermott - Introduction to Audition Part 2
Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex
Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology Part 1
Lecture 7.5: Hynek Hermansky - Auditory Perception in Speech Technology Part 2
Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge
Lecture 8.2: John Leonard - Mapping, Localization, & Self-Driving Vehicles
Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots
Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration
Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot
Lecture 8.6: iCub Team - Overview of Research on the iCub Robot
Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks
Lecture 9.2: Haim Sompolinsky - Sensory Representations in Deep Networks
Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning