MIT OpenCourseWare

Browse materialsMIT 9.66J · Fall 2004

Computational Cognitive Science

Subjects: Engineering · Science

Art: Santiago Ramón y Cajal, Cajal - a purkinje neuron from the human cerebellum, circa 1900 · Public domain

Instructor: Prof. Joshua Tenenbaum
Term: Fall 2004
Level: Undergraduate
What's inside: 1 lecture note · 1 reading · 27 other files · 4 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 9.66J Computational Cognitive Science, Fall 2004, Prof. Joshua Tenenbaum — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

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Lecture Notes

24 files

Projects

Study Materials

Handout on MATLAB®, Statistics, and Linear Regression (Courtesy of Justin Werfel. Used with permission.)PDF Download

MIT OpenCourseWare, Prof. Joshua Tenenbaum, 9.66J Computational Cognitive Science, Fall 2004.

License: CC BY-NC-SA 4.0 · Edition: Fall 2004 · Open the original course on OCW ↗

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Computational Cognitive Science

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Projects

Study Materials

Lecture 1: Introduction

Lecture 2: Foundations of Inductive Learning

Lecture 3: Knowledge Representation: Spaces, Trees, Features

Lecture 4: Knowledge Representation: Language and Logic 1

Lecture 5: Knowledge Representation: Language and Logic 2

Lecture 6: Knowledge Representation: Great Debates 1

Lecture 7: Knowledge Representation: Great Debates 2

Lecture 8: Basic Bayesian Inference

Lecture 9: Graphical Models and Bayes Nets

Lecture 10: Simple Bayesian Learning 1

Lecture 11: Simple Bayesian Learning 2

Lecture 12: Probabilistic Models for Concept Learning and Categorization 1

Lecture 13: Probabilistic Models for Concept Learning and Categorization 2

Lecture 14: Unsupervised and Semi-supervised Learning

Lecture 15: Non-parametric Classification: Exemplar Models and Neural Networks 1

Lecture 16: Non-parametric Classification: Exemplar Models and Neural Networks 2

Lecture 17: Controlling Complexity and Occam’s Razor 1

Lecture 18: Controlling Complexity and Occam’s Razor 2

Lecture 19: Intuitive Biology and the Role of Theories

Lecture 20: Learning Domain Structures 1

Lecture 21: Learning Domain Structures 2

Lecture 22: Causal Learning

Lecture 23: Causal Theories 1

Lecture 24: Causal Theories 2

Lecture 25: Project Presentations