Catalog

Brains, Minds and Machines Summer Course

Brain and Cognitive Sciences MIT CC BY-NC-SA 4.0 52 lectures

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

  1. 1 Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, & Machines
  2. 2 Nick Cheney: Capturing Neural Plasticity in Deep Networks
  3. 3 Danny Jeck: Impact of Attention on Cortical Models of Visual Recognition
  4. 4 Alon Baram & Laurie Bayet: Learning to Recognize Digits and Faces from Few Examples
  5. 5 David Rolnick & Ishita Dasgupta: Modeling Dynamic Memory with Hopfield Networks
  6. 6 Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience
  7. 7 Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback
  8. 8 Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1
  9. 9 Lecture 1.4: James DiCarlo - Neural Mechanisms of Recognition Part 2
  10. 10 Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence
  11. 11 Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1
  12. 12 Lecture 1.7: Matt Wilson - Hippocampus, Memory, & Sleep Part 2
  13. 13 Seminar 1: Larry Abbott - Mind in the Fly Brain
  14. 14 Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1
  15. 15 Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2
  16. 16 Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3
  17. 17 Lecture 3.1: Liz Spelke - Cognition in Infancy Part 1
  18. 18 Lecture 3.2: Liz Spelke - Cognition in Infancy Part 2
  19. 19 Lecture 3.3: Alia Martin - Developing an Understanding of Communication
  20. 20 Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information
  21. 21 Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit
  22. 22 Lecture 3.5: Josh Tenenbaum - The Child as Scientist
  23. 23 Unit 3 Debate: Tomer Ullman & Laura Schulz
  24. 24 Lecture 4.1: Shimon Ullman - Development of Visual Concepts
  25. 25 Lecture 4.2: Shimon Ullman - Atoms of Recognition
  26. 26 Lecture 4.3: Aude Oliva - Predicting Visual Memory
  27. 27 Seminar 4.1: Eero Simoncelli - Probing Sensory Representations
  28. 28 Seminar 4.2: Amnon Shashua - Applications of Vision
  29. 29 Lecture 5.1: Boris Katz - Vision and Language
  30. 30 Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again
  31. 31 Lecture 5.3: Patrick Winston - Story Understanding
  32. 32 Seminar 5: Tom Mitchell - Neural Representations of Language
  33. 33 Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence
  34. 34 Lecture 6.2: Ken Nakayama - The Social Mind
  35. 35 Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1
  36. 36 Lecture 6.4: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 2
  37. 37 Lecture 7.1: Josh McDermott - Introduction to Audition Part 1
  38. 38 Lecture 7.2: Josh McDermott - Introduction to Audition Part 2
  39. 39 Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex
  40. 40 Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology Part 1
  41. 41 Lecture 7.5: Hynek Hermansky - Auditory Perception in Speech Technology Part 2
  42. 42 Unit 7 Panel: Vision and Audition
  43. 43 Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge
  44. 44 Lecture 8.2: John Leonard - Mapping, Localization, & Self-Driving Vehicles
  45. 45 Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots
  46. 46 Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration
  47. 47 Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot
  48. 48 Lecture 8.6: iCub Team - Overview of Research on the iCub Robot
  49. 49 Unit 8 Panel: Robotics
  50. 50 Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks
  51. 51 Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning
  52. 52 Lecture 9.2: Haim Sompolinsky - Sensory Representations in Deep Networks

Course materials