PSYCH 541 - Advanced Topics in Cognition and Cognitive Neuroscience
Section: 001 Behavioral Data Mining
Term: FA 2017
Subject: Psychology (PSYCH)
Department: LSA Psychology
Requirements & Distribution:
With permission of instructor.
Advisory Prerequisites:
PSYCH 240.
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
May be repeated for a maximum of 6 credit(s). May be elected more than once in the same term.
Primary Instructor:

This advanced undergraduate/graduate seminar will address data analytic methods in social and behavioral science. Instead of exhaustively covering machine learning algorithms and technical aspects, the course would rather focus on understanding and drawing inference about data in behavioral setting. Topics will cover: measurement structure, preference structure, order structure, clustering, aggregation, similarity, neighbor, issues of model fitting and overfitting, Bayesian inference, etc. Students will be assigned into groups to work on data analytics arising from behavioral finance, transportation, education, and social networks.

Course Requirements:

Confirmation of enrollment: All students would need to be present at the Sept 7th class, where the instructor would review each student’s background to ensure suitability. Special requests from students (including overrides) will also be addressed during that time.

Intended Audience:

The course is suitable for advanced undergraduate students in psychology, economics, information science, business, etc, who have taken statistics courses or enrolled in “data science”. This course will also accept graduate students who are interested in data science. While computer programming skills, familiarity with R package, or other prior data science is a plus, they are not required.

PSYCH 541 - Advanced Topics in Cognition and Cognitive Neuroscience
Schedule Listing
001 (SEM)
Th 6:00PM - 9:00PM
Note: Topic: Behavioral Data Mining. Open to Jr/Sr undergrads who have taken a statistics course and open to graduate students.
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