Instructor Insights

Course Overview

Elements of the Brains, Minds, and Machines (BMM) summer course are integrated into the MIT course, 9.523 Aspects of a Computational Theory of Intelligence. This page focuses on 9.523 as it was taught by Profs. Tomaso Poggio, Shimon Ullman, Patrick Winston, and Ellen Hildreth (Wellesley College) in Fall 2015. It also provides insight into how materials from the Brains, Minds, and Machines (BMM) summer course can be used to form the basis of full-semester courses at other institutions.

Like the summer course, 9.523 Aspects of a Computational Theory of Intelligence introduced students to research on intelligence that integrates the perspectives of neuroscience, cognitive science, and computation, with the aim of understanding how intelligent behavior is produced by the brain and how it can be replicated in machines.

An archive of the Fall 2015 website is available, which includes course information, schedule, assignments, and readings. 

Course Outcomes

Course Goals for Students

  • Learn to think critically about how to integrate empirical and computational approaches to the study of intelligence
  • Learn about current interdisciplinary research on intelligence, including research in the Center for Brains, Minds, and Machines
  • Develop skills in written and oral research communication 

Possibilities for Further Study/Careers

This course helps to prepare students for research in intelligence science, combining empirical and computational methods, which may be pursued in academic or industry careers.

Instructor Interview

"[This course had] one of the best formats of class structure. The concepts were reiterated three times in a week through different modes – reading, writing response, student presentations, expert presentations. The format of structuring the class was very significant in imparting knowledge of the subjects, even to students that were not familiar with the field or had no background skills in computer science."
— 9.523 student, Fall 2015

In the following pages, Prof. Ellen Hildreth reflects on how 9.523 Aspects of a Computational Theory of Intelligence was taught and how the Brains, Minds, and Machines (BMM) summer course materials can be used to design other full-semester courses.

Curriculum Information


There are no formal prerequisites, but introductory courses in the following areas enable a deeper understanding of the material: computer science, neuroscience, cognitive science, linear algebra, probability and statistics.

Requirements Satisfied

9.523 can be applied toward graduate degrees in Electrical Engineering and Computer Science (Course 6) and Brain and Cognitive Sciences (Course 9), but is not required.


Every fall semester


The students’ grades were based on the following activities:

  • 10% Class participation
  • 15% Class presentation of reading
  • 40% Assignments
  • 35% Final project

Student Information


18 students

Breakdown by Year

Graduate and undergraduate students (sophomores, juniors, and seniors)

Breakdown by Major

Students were from several majors, including Brain and Cognitive Sciences, Electrical Engineering and Computer Science, Mathematics, and Architecture.

How Student Time Was Spent

During an average week, students were expected to spend 12 hours on the course, roughly divided as follows:


  • Met 1 time per week for 2 hours per session; 12 sessions total.
  • Lectures were offered by faculty of the Center for Brains, Minds, and Machines who also taught the summer course.
  • Lectures emphasized current research while weaving in historical background to provide context for the research and introducing key empirical and theoretical methods.


  • Met 1 time per week for 1 hour per session; 12 sessions total.
  • In each recitation, a pair of students presented the weekly readings and led a class discussion.
  • Specific topics were drawn from the summer course and included visual recognition, learning complex visual concepts, audition, the integration of vision and language, story understanding, the development of intelligence from infancy, probabilistic models of inference and learning, deep learning, and spatial navigation.

Out of Class

  • Weekly reading assignments from the research literature
  • Weekly writing assignments that asked students to respond to a key question about the reading and to formulate a new question
  • Extended assignments that applied key concepts learned in the course
  • Pair work to prepare presentation and discussion of readings
  • Final project preparation; projects incorporated a critical analysis of current research or work on an original research problem

Course Info

Learning Resource Types

theaters Other Video
theaters Lecture Videos
notes Lecture Notes
group_work Projects
co_present Instructor Insights