Overview
This course will introduce the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples will be drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena.
Instructor:
Noah Goodman (ngoodman at stanford dot edu)
TAs:
Long Ouyang (louyang at stanford dot edu),
Daniel Hawthorne (djthorne at stanford dot edu).
Meeting time: T Th 1:30-3.
Meeting place: Cummings Art building, room Art 2. (Note new room!!)
Discussion: You can post questions and comments to this Piazza group.
Office hours:
TAs will hold office hours in 330 Jordan Hall, Mon 11-12 and Thurs 9:30-10:30.
Instructor will meet with students after class or by appointment.
Assignments and grading
Students (both registered and auditing) will be expected to do assigned readings before class.
Registered students will be graded based on:
- 30% Class participation.
- 35% Homework.
- 35% Final project. (Project instructions).
Readings
Readings for each week will be linked from the calendar section. (In some cases these will require an SUNet ID to access. See the instructor in case of trouble.) Readings will be drawn from the web-book Probabilistic Models of Cognition and selected research papers.
Pre-requisites
There are no formal pre-requisites for this class. However, the course will move relatively quickly and have technical content. Students should be already familiar with the basics of probability and programming (or be willing to learn this background on their own).