1 Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines 2 Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience 3 Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback 4 Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1 5 Lecture 1.4: Neural Mechanisms of Recognition, Part 2 6 Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence 7 Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1 8 Lecture 1.7: Hippocampus, Memory, & Sleep, Part 2 9 Seminar 1: Larry Abbott - Mind in the Fly Brain 10 Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1 11 Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2 12 Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3 13 Lecture 3.1: Liz Spelke - Cognition in Infancy (Part 1) 14 Lecture 3.2: Cognition in Infancy, Part 2 15 Lecture 3.3: Alia Martin - Developing an Understanding of Communication 16 Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information 17 Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit 18 Lecture 3.5: Josh Tenenbaum - The Child as Scientist 19 Unit 3 Debate: Tomer Ullman and Laura Schulz 20 Lecture 4.1: Shimon Ullman - Development of Visual Concepts 21 Lecture 4.2: Shimon Ullman - Atoms of Recognition 22 Lecture 4.3. Aude Oliva - Predicting Visual Memory 23 Seminar 4.1: Eero Simoncelli: Probing Sensory Representations 24 Seminar 4.2: Anmon Shashua - Applications of Vision 25 Lecture 5.1: Vision and Language 26 Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again 27 Lecture 5.3: Patrick Winston - Story Understanding 28 Seminar 5: Tom Mitchell - Neural Representations of Language 29 Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence 30 Lecture 6.2: Ken Nakayama - The Social Mind 31 Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1 32 Lecture 6.4: MVPA: Window on the Mind via fMRI, Part 2 33 Lecture 7.1: Josh McDermott - Introduction to Audition, Part 1 34 Lecture 7.2: Josh McDermott - Introduction to Audition, Part 2 35 Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex 36 Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology, Part 1 37 Lecture 7.5: Hynek Hermansky - Auditory Perception in Speech Technology, Part 2 38 Unit 7 Panel: Vision and Audition 39 Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge 40 Lecture 8.2: John Leonard - Mapping, Localization and Self Driving Vehicles 41 Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots 42 Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration 43 Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot 44 Lecture 8.6: iCub Team - Overview of Research on the iCub Robot 45 Unit 8 Panel: Robotics 46 Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks 47 Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning 48 Lecture 9.2: Haim Sompolinksy - Sensory Representations in Deep Networks 49 Tutorial 1: Leyla Isik - Introduction to Visual Neuroscience 50 Tutorial 3.1: Lorenzo Rosasco - Machine Learning Part 1 51 Tutorial 3.2: Lorenzo Rosasco - Machine Learning Part 2 52 Tutorial 3.3: Lorenzo Rosasco - Machine Learning Part 3 53 Tutorial 4: Ethan Meyers - Understanding Neural Content via Population Decoding 54 Tutorial 5.1: Tomer Ullman - Church Programming Language Part 1 55 Tutorial 5.2: Tomer Ullman - Church Programming Language Part 2 56 Tutorial 6: Tomer Ullman - Amazon Mechanical Turk 57 Nick Cheney: Capturing Neural Plasticity in Deep Networks 58 Danny Jeck: Impact of Attention on Cortical Models of Visual Recognition 59 Alon Baram & Laurie Bayet: Learning to Recognize Digits and Faces from Few Examples 60 David Rolnick & Ishita Dasgupta: Modeling Dynamic Memory with Hopfield Networks