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RES.9-003

Brains, Minds and Machines Summer Course

Prof. Tomaso Poggio, Prof. Gabriel Kreiman | Summer 2015

Data Science, Analytics & Computer Technology AI Machine Learning Computer Science Social Sciences Psychology Science & Math Biology

前往原始課程

CC BY-NC-SA 4.0

課程簡介

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 Brains, Minds and Machines Summer Course offered annually at the Marine Biological Laboratory in Woods Hole, MA, taught by faculty affiliated with the 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.

課程資訊

來源	MIT 開放式課程
科系	Brain and Cognitive Sciences
語言	English
影片數	60

課程影片 (60)

Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines

Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines

Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience

Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience

Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback

Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback

Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1

Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1

Lecture 1.4: Neural Mechanisms of Recognition, Part 2

Lecture 1.4: Neural Mechanisms of Recognition, Part 2

Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence

Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence

Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1

Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1

Lecture 1.7: Hippocampus, Memory, & Sleep, Part 2

Lecture 1.7: Hippocampus, Memory, & Sleep, Part 2

Seminar 1: Larry Abbott - Mind in the Fly Brain

Seminar 1: Larry Abbott - Mind in the Fly Brain

Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1

Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1

Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2

Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2

Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3

Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3

Lecture 3.1: Liz Spelke - Cognition in Infancy (Part 1)

Lecture 3.1: Liz Spelke - Cognition in Infancy (Part 1)

Lecture 3.2: Cognition in Infancy, Part 2

Lecture 3.2: Cognition in Infancy, Part 2

Lecture 3.3: Alia Martin - Developing an Understanding of Communication

Lecture 3.3: Alia Martin - Developing an Understanding of Communication

Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information

Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information

Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit

Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit

Lecture 3.5: Josh Tenenbaum - The Child as Scientist

Lecture 3.5: Josh Tenenbaum - The Child as Scientist

Unit 3 Debate: Tomer Ullman and Laura Schulz

Unit 3 Debate: Tomer Ullman and Laura Schulz

Lecture 4.1: Shimon Ullman - Development of Visual Concepts

Lecture 4.1: Shimon Ullman - Development of Visual Concepts

Lecture 4.2: Shimon Ullman - Atoms of Recognition

Lecture 4.2: Shimon Ullman - Atoms of Recognition

Lecture 4.3. Aude Oliva - Predicting Visual Memory

Lecture 4.3. Aude Oliva - Predicting Visual Memory

Seminar 4.1: Eero Simoncelli: Probing Sensory Representations

Seminar 4.1: Eero Simoncelli: Probing Sensory Representations

Seminar 4.2: Anmon Shashua - Applications of Vision

Seminar 4.2: Anmon Shashua - Applications of Vision

Lecture 5.1: Vision and Language

Lecture 5.1: Vision and Language

Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again

Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again

Lecture 5.3: Patrick Winston - Story Understanding

Lecture 5.3: Patrick Winston - Story Understanding

Seminar 5: Tom Mitchell - Neural Representations of Language

Seminar 5: Tom Mitchell - Neural Representations of Language

Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence

Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence

Lecture 6.2: Ken Nakayama - The Social Mind

Lecture 6.2: Ken Nakayama - The Social Mind

Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1

Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1

Lecture 6.4: MVPA: Window on the Mind via fMRI, Part 2

Lecture 6.4: MVPA: Window on the Mind via fMRI, Part 2

Lecture 7.1: Josh McDermott - Introduction to Audition, Part 1

Lecture 7.1: Josh McDermott - Introduction to Audition, Part 1

Lecture 7.2: Josh McDermott - Introduction to Audition, Part 2

Lecture 7.2: Josh McDermott - Introduction to Audition, Part 2

Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex

Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex

Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology, Part 1

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 7.5: Hynek Hermansky - Auditory Perception in Speech Technology, Part 2

Unit 7 Panel: Vision and Audition

Unit 7 Panel: Vision and Audition

Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge

Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge

Lecture 8.2: John Leonard - Mapping, Localization and Self Driving Vehicles

Lecture 8.2: John Leonard - Mapping, Localization and Self Driving Vehicles

Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots

Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots

Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration

Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration

Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot

Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot

Lecture 8.6: iCub Team - Overview of Research on the iCub Robot

Lecture 8.6: iCub Team - Overview of Research on the iCub Robot

Unit 8 Panel: Robotics

Unit 8 Panel: Robotics

Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks

Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks

Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning

Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning

Lecture 9.2: Haim Sompolinksy - Sensory Representations in Deep Networks

Lecture 9.2: Haim Sompolinksy - Sensory Representations in Deep Networks

Tutorial 1: Leyla Isik - Introduction to Visual Neuroscience

Tutorial 1: Leyla Isik - Introduction to Visual Neuroscience

Tutorial 3.1: Lorenzo Rosasco - Machine Learning Part 1

Tutorial 3.1: Lorenzo Rosasco - Machine Learning Part 1

Tutorial 3.2: Lorenzo Rosasco - Machine Learning Part 2

Tutorial 3.2: Lorenzo Rosasco - Machine Learning Part 2

Tutorial 3.3: Lorenzo Rosasco - Machine Learning Part 3

Tutorial 3.3: Lorenzo Rosasco - Machine Learning Part 3

Tutorial 4: Ethan Meyers - Understanding Neural Content via Population Decoding

Tutorial 4: Ethan Meyers - Understanding Neural Content via Population Decoding

Tutorial 5.1: Tomer Ullman - Church Programming Language Part 1

Tutorial 5.1: Tomer Ullman - Church Programming Language Part 1

Tutorial 5.2: Tomer Ullman - Church Programming Language Part 2

Tutorial 5.2: Tomer Ullman - Church Programming Language Part 2

Tutorial 6: Tomer Ullman - Amazon Mechanical Turk

Tutorial 6: Tomer Ullman - Amazon Mechanical Turk

Nick Cheney: Capturing Neural Plasticity in Deep Networks

Nick Cheney: Capturing Neural Plasticity in Deep Networks

Danny Jeck: Impact of Attention on Cortical Models of Visual Recognition

Danny Jeck: Impact of Attention on Cortical Models of Visual Recognition

Alon Baram & Laurie Bayet: Learning to Recognize Digits and Faces from Few Examples

Alon Baram & Laurie Bayet: Learning to Recognize Digits and Faces from Few Examples

David Rolnick & Ishita Dasgupta: Modeling Dynamic Memory with Hopfield Networks

David Rolnick & Ishita Dasgupta: Modeling Dynamic Memory with Hopfield Networks