MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 (English)

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MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 By Lecture Notes together!

Lecture 1: Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines

Lecture 2: Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience

Lecture 3: Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback

Lecture 4: Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1

Lecture 5: Lecture 1.4: Neural Mechanisms of Recognition, Part 2

Lecture 6: Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence

Lecture 7: Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1

Lecture 8: Lecture 1.7: Hippocampus, Memory, & Sleep, Part 2

Lecture 9: Seminar 1: Larry Abbott - Mind in the Fly Brain

Lecture 10: Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1

Lecture 11: Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2

Lecture 12: Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3

Lecture 13: Lecture 3.1: Liz Spelke - Cognition in Infancy (Part 1)

Lecture 14: Lecture 3.2: Cognition in Infancy, Part 2

Lecture 15: Lecture 3.3: Alia Martin - Developing an Understanding of Communication

Lecture 16: Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information

Lecture 17: Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit

Lecture 18: Lecture 3.5: Josh Tenenbaum - The Child as Scientist

Lecture 19: Unit 3 Debate: Tomer Ullman and Laura Schulz

Lecture 20: Lecture 4.1: Shimon Ullman - Development of Visual Concepts

Lecture 21: Lecture 4.2: Shimon Ullman - Atoms of Recognition

Lecture 22: Lecture 4.3. Aude Oliva - Predicting Visual Memory

Lecture 23: Seminar 4.1: Eero Simoncelli: Probing Sensory Representations

Lecture 24: Seminar 4.2: Anmon Shashua - Applications of Vision

Lecture 25: Lecture 5.1: Vision and Language

Lecture 26: Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again

Lecture 27: Lecture 5.3: Patrick Winston - Story Understanding

Lecture 28: Seminar 5: Tom Mitchell - Neural Representations of Language

Lecture 29: Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence

Lecture 30: Lecture 6.2: Ken Nakayama - The Social Mind

Lecture 31: Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1

Lecture 32: Lecture 6.4: MVPA: Window on the Mind via fMRI, Part 2

Lecture 33: Lecture 7.1: Josh McDermott - Introduction to Audition, Part 1

Lecture 34: Lecture 7.2: Josh McDermott - Introduction to Audition, Part 2

Lecture 35: Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex

Lecture 36: Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology, Part 1

Lecture 37: Lecture 7.5: Hynek Hermansky - Auditory Perception in Speech Technology, Part 2

Lecture 38: Unit 7 Panel: Vision and Audition

Lecture 39: Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge

Lecture 40: Lecture 8.2: John Leonard - Mapping, Localization and Self Driving Vehicles

Lecture 41: Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots

Lecture 42: Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration

Lecture 43: Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot

Lecture 44: Lecture 8.6: iCub Team - Overview of Research on the iCub Robot

Lecture 45: Unit 8 Panel: Robotics

Lecture 46: Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks

Lecture 47: Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning

Lecture 48: Lecture 9.2: Haim Sompolinksy - Sensory Representations in Deep Networks

Lecture 49: Tutorial 1: Leyla Isik - Introduction to Visual Neuroscience

Lecture 50: Tutorial 3.1: Lorenzo Rosasco - Machine Learning Part 1

Lecture 51: Tutorial 3.2: Lorenzo Rosasco - Machine Learning Part 2

Lecture 52: Tutorial 3.3: Lorenzo Rosasco - Machine Learning Part 3

Lecture 53: Tutorial 4: Ethan Meyers - Understanding Neural Content via Population Decoding

Lecture 54: Tutorial 5.1: Tomer Ullman - Church Programming Language Part 1

Lecture 55: Tutorial 5.2: Tomer Ullman - Church Programming Language Part 2

Lecture 56: Tutorial 6: Tomer Ullman - Amazon Mechanical Turk

Lecture 57: Nick Cheney: Capturing Neural Plasticity in Deep Networks

Lecture 58: Danny Jeck: Impact of Attention on Cortical Models of Visual Recognition

Lecture 59: Alon Baram & Laurie Bayet: Learning to Recognize Digits and Faces from Few Examples

Lecture 60: David Rolnick & Ishita Dasgupta: Modeling Dynamic Memory with Hopfield Networks