Instructor: Mai Nguyen, mlnguyen@princeton.edu
Meeting time: MW, 2-3:30pm from June 10-July 29 on zoom
Office hours: by appointment, very flexible
This class is an introduction to scientific computing using MATLAB for students with little to no experience with programming or statistics. Weeks 1-4 will focus on programming fundamentals, and Weeks 5-8 will walk through a simple data analysis pipeline and touch on advanced topics. This is a highly compressed course and will not cover everything you will need to know to do your research. The goal is to cover the basics and learn “enough” to get started on figuring out what you need to do, while emphasizing best practices.
Class structure
- Combination of lecture and working through class examples
- Weekly assignments (2-3 hours, maybe longer for later topics) assigned Friday, due following Tuesday. I’ll provide written feedback/comments on assignments and will be available for one-one-one meetings
Data for several homework assignments and lectures were generously shared by members of the Princeton Department of Psychology and Princeton Neuroscience Institute as indicated below. Data are shared here with permission of the authors.
- Week 2, HW Problem 3: Data from M Nguyen (mlnguyen [at] princeton.edu), adapted from "Teacher-Student neural coupling during teaching and learning" [link]
- Week 3, HW Problem 1: Data from A Kurosu (akurosu [at] princeton.edu) and Yoko Urano, adapted from work in prep.
- Week 3, HW Problem 2: Data from Y Yeshurun (yaaray [at] tauex.tau.ac.il), adapted from "Same story, different story: the neural representation of interpretive frameworks" [link]
- Week 5, Lecture 8: Data from N Rouhani (nrouhani [at] princeton.edu), adapted from "Dissociable effects of surprising rewards on learning and memory" [link]
- Week 5, Lecture 9: Data from S Wilterson (wilterson [at] princeton.edu), adapted from "Relative sensitivity of explicit reaiming and implicit motor adaptation" [link]
- Week 5, HW Problems 1-3: Data from M Nguyen (mlnguyen [at] princeton.edu), adapted from "Teacher-Student neural coupling during teaching and learning" [link]
- Many thanks to Elise Piazza (epiazza [at] princeton.edu) and Catalin Iordan (mci [at] princeton.edu for their guest lecture on classifiers on July 15! Work on using classifiers to show differences in timbre for adult vs infant directed speech is "Mothers Consistently Alter Their Unique Vocal Fingerprints When Communicating with Infants" [link].
- Many thanks to Zaid Zada (zzada [at] princeton.edu] for their guest lecture on natural language processing on July 22
- Many thanks to Sam Nastase (snastase [at] princeton.edu] for their guest lecture on open science, code reproducibility, and GitHub on July 24