# playlist: click the video's upper-left icon
source: MIT OpenCourseWare
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
View the complete course: https://ocw.mit.edu/RES-9-003SU15
Instructor: Prof. Tomaso Poggio (Course Director, MIT), Prof. Gabriel Kreiman (Course Director, Harvard)
This collection contains all of the lecture, seminar and panel discussion videos from the nine units comprising the course.
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
1 11:12 Lecture 0: Tomaso Poggio - Introduction to Brains, Minds, and Machines
2 46:09 Lecture 1.1: Nancy Kanwisher - Human Cognitive Neuroscience
3 55:18 Lecture 1.2: Gabriel Kreiman - Computational Roles of Neural Feedback
4 1:02:37 Lecture 1.3: James DiCarlo - Neural Mechanisms of Recognition Part 1
5 51:20 Lecture 1.4: Neural Mechanisms of Recognition, Part 2
6 59:31 Lecture 1.5: Winrich Freiwald - Primates, Faces, & Intelligence
7 51:36 Lecture 1.6: Matt Wilson - Hippocampus, Memory, & Sleep Part 1
8 28:12 Lecture 1.7: Hippocampus, Memory, & Sleep, Part 2
9 52:45 Seminar 1: Larry Abbott - Mind in the Fly Brain
10 1:01:27 Lecture 2.1: Josh Tenenbaum - Computational Cognitive Science Part 1
11 1:10:13 Lecture 2.2: Josh Tenenbaum - Computational Cognitive Science Part 2
12 1:06:49 Lecture 2.3: Josh Tenenbaum - Computational Cognitive Science Part 3
13 1:03:15 Lecture 3.1: Liz Spelke - Cognition in Infancy (Part 1)
14 45:27 Lecture 3.2: Cognition in Infancy, Part 2
15 46:18 Lecture 3.3: Alia Martin - Developing an Understanding of Communication
16 55:32 Lecture 3.4: Laura Schulz - Childrens' Sensitivity to Cost and Value of Information
17 39:11 Seminar 3: Jessica Sommerville - Infants' Sensitivity to Cost and Benefit
18 30:27 Lecture 3.5: Josh Tenenbaum - The Child as Scientist
19 1:16:16 Unit 3 Debate: Tomer Ullman and Laura Schulz
20 58:03 Lecture 4.1: Shimon Ullman - Development of Visual Concepts
21 49:32 Lecture 4.2: Shimon Ullman - Atoms of Recognition
22 59:53 Lecture 4.3. Aude Oliva - Predicting Visual Memory
23 56:45 Seminar 4.1: Eero Simoncelli: Probing Sensory Representations
24 56:13 Seminar 4.2: Anmon Shashua - Applications of Vision
25 54:44 Lecture 5.1: Vision and Language
26 1:05:06 Lecture 5.2: Andrei Barbu - From Language to Vision and Back Again
27 1:00:31 Lecture 5.3: Patrick Winston - Story Understanding
28 46:49 Seminar 5: Tom Mitchell - Neural Representations of Language
29 27:49 Lecture 6.1: Nancy Kanwisher - Introduction to Social Intelligence
30 47:11 Lecture 6.2: Ken Nakayama - The Social Mind
31 52:28 Lecture 6.3: Rebecca Saxe - MVPA: Window on the Mind via fMRI Part 1
32 34:08 Lecture 6.4: MVPA: Window on the Mind via fMRI, Part 2
33 1:05:52 Lecture 7.1: Josh McDermott - Introduction to Audition, Part 1
34 45:45 Lecture 7.2: Josh McDermott - Introduction to Audition, Part 2
35 17:39 Lecture 7.3: Nancy Kanwisher - Human Auditory Cortex
36 1:03:56 Lecture 7.4: Hynek Hermansky - Auditory Perception in Speech Technology, Part 1
37 1:01:33 Lecture 7.5: Hynek Hermansky - Auditory Perception in Speech Technology, Part 2
38 1:06:58 Unit 7 Panel: Vision and Audition
39 26:30 Lecture 8.1: Russ Tedrake - MIT's Entry in the DARPA Robotics Challenge
40 31:02 Lecture 8.2: John Leonard - Mapping, Localization and Self Driving Vehicles
41 32:15 Lecture 8.3: Tony Prescott - Control Architecture in Mammals and Robots
42 24:03 Lecture 8.4: Stefanie Tellex - Human-Robot Collaboration
43 34:05 Lecture 8.5: Giorgio Metta - Introduction to the iCub Robot
44 1:05:36 Lecture 8.6: iCub Team - Overview of Research on the iCub Robot
45 55:09 Unit 8 Panel: Robotics
46 46:13 Lecture 9.1: Tomaso Poggio - iTheory: Visual Cortex & Deep Networks
47 1:03:42 Seminar 9: Surya Ganguli - Statistical Physics of Deep Learning
48 53:14 Lecture 9.2: Haim Sompolinksy - Sensory Representations in Deep Networks
49 19:55 Tutorial 1: Leyla Isik - Introduction to Visual Neuroscience
50 58:34 Tutorial 3.1: Lorenzo Rosasco - Machine Learning Part 1
51 55:04 Tutorial 3.2: Lorenzo Rosasco - Machine Learning Part 2
52 41:43 Tutorial 3.3: Lorenzo Rosasco - Machine Learning Part 3
53 56:22 Tutorial 4: Ethan Meyers - Understanding Neural Content via Population Decoding
54 52:47 Tutorial 5.1: Tomer Ullman - Church Programming Language Part 1
55 53:55 Tutorial 5.2: Tomer Ullman - Church Programming Language Part 2
56 41:38 Tutorial 6: Tomer Ullman - Amazon Mechanical Turk
57 2:44 Diego Mendoza-Halliday: iCub Robot Plays the Piano
58 3:29 Nick Cheney: Capturing Neural Plasticity in Deep Networks
59 2:39 Danny Jeck: Impact of Attention on Cortical Models of Visual Recognition
60 4:21 Alon Baram & Laurie Bayet: Learning to Recognize Digits and Faces from Few Examples
61 3:46 David Rolnick & Ishita Dasgupta: Modeling Dynamic Memory with Hopfield Networks
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