COGS109: Modeling and Data Analysis

UCSD Cognitive Science course : Modeling and Data Analysis

This course will be taught by Alex Simpkins Ph.D. in SS1 2023.

The Teaching Assistant is Sagarika Sardesai, who is as of 2023 a Masters Student at UCSD.

The course web page is posted at the link below, and is the source of the most up to date syllabus/information/files, etc:

Course web site

and some materials available here on github at this directory

Course Description

This course will cover modeling and data analysis for Cognitive scientists, scientists of other disciplines, and Engineers. Emphasis will be given to the Cognitive perspective, with examples drawn from topics relevant to cognitive scientists.

Topics may include programming tools, matlab, linear algebra, linear regression, nonlinear regression (polynomial and exponential fits), basic statistical analysis (mean, standard deviation, mode, median, variance, covariance, correlation, hypothesis testing), numerical solution of differential equations, optimization, curve fitting, and data visualization. Neural networks will be included as well if time permits.

The objective of this course is to give fundamental tools to the student which will allow the student to effectively analyze data from experiments, extract information, understand standard analyis tools commonly found in the literature of science and engineering, and communicate results effectively. The student will also be given many resources from which to draw in the future, thus allowing them to expand their knowledge and skills.

Theory lectured on in class will be followed up with readings to expand on the concepts, homeworks to give experience with the techniques, and additional references/readings of research work in the field applying these techniques (demonstrating how these techniques are applied in real life).

Prerequisites

-Functional Brain

-CogSci 18 or consent of instructor

drsimpkins-teaching/cogs109

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