PSYC 4101 Research Practicum in Human and Artificial Intelligence

This course is a practical introduction to the convergence between psychology, neuroscience, and artificial intelligence. Ideas from psychology and neuroscience have inspired the development of artificial neural networks. Their current instantiations, deep neural networks, are having a profound impact on computer science and society, but also on the study of the brain and cognition. This course starts with a brief introduction to the roots of artificial intelligence in psychology and neuroscience, followed by a brief, accessible introduction to the mathematical foundations of deep neural networks. In the remainder of the course, students will learn to use python and pytorch and will work in groups to implement and train deep neural networks to build models of behavior or of the brain. Prior experience with computer programming is highly recommended; students with no experience in programming are encouraged to meet the instructor before enrolling.

Course overview

Department
Psychology and Neuroscience
School
MCAS
Credits
3
Level
Undergraduate
Offered
Annually

Catalog details

Prerequisites
PSYC1120 or BIOL2300 and BZAN1135 and ECON1151 and SOCY2200 and PHCG3560

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Requirements fulfilled

Requirement eligibility can vary by school, cohort, and section. Confirm the selected section in EagleApps or with an advisor.

Course evaluations

Response-weighted averages from Boston College’s official course evaluations (Avalanche/Blue).

Course rating
4.21 / 5 · Very Good
37 responses
Instructor rating
4.54 / 5 · Excellent
37 responses
Terms evaluated
3
Most recent: Fall 2024

Scales: overall ratings run from 1 (poor) to 5 (excellent); agreement questions from 1 (strongly disagree) to 5 (strongly agree); weekly effort from 1 (up to 1 hour) to 5 (10 or more hours).

Evaluations by term and instructor
TermInstructorCourse overallInstructor overallWell organizedClear learning objectivesIntellectually challengingHelpful assignmentsApplicable beyond courseAttendance necessaryWeekly effortResponses
Fall 2024Stefano Anzellotti4.44.74.74.64.95.04.95.01–3 hrs/week10 of 11
Fall 2023Stefano Anzellotti—————————13 of 16
Fall 2022Stefano Anzellotti3.64.24.24.34.84.44.14.41–3 hrs/week14 of 16

Data freshness

Course and evaluation data last updated 2026-10-09. Source details and limitations are documented in Data Sources and Methodology.

Instructors

Sections

  • Section 01
    Spring 2027 · Stefano Anzellotti · Mcguinn Hall 332 F 01:00PM-03:30PM · Offered