posted on May 15, 2022

This document provides resources to help any individual from any background acquire the necessary skills for engaging in the research conducted at the Human and Machine Cognition lab at the University of Tübingen. The goal is to remove barriers to science, by making it as accessible as possible for motivated individuals.

- A First Course in Linear Algebra
- Mathematical Tools for Neuroscience (Jonathan Pillow, Princeton)
- Advanced review on linear gaussian systems (Sam Roweis)
- Linear Algebra (Gilbert Strang, MIT)

- Probability and Causality in Human Cognition (Josh Tenenbuam, MIT)
- Stat 110 by Joe Blitzstein (YouTube lectures + book + hw + solutions)
- Probability basics for ML (slides; Chris Cremer, University of Toronto)

- Statistical Methods for Behavioral and Social Sciences (Tobias Gerstenberg, Stanford)
- Statistical Rethinking (Richard McElreath, Max Planck Institute)
- Regression and other stories (Andrew Gelman, Jennifer Hill, Aki Vehtari)

- Shell commands, GIT, text editors, etc… (MIT course, available in multiple languages)

- Setting up Python environment
- https://www.learnpython.org/
- Python for data science
- Object Oriented Programming (Youtube playlist)

- Download and install R
- R Cookbook (beginners guide)
- Data visualization (ggplot)
- R for data science (textbook and interactive notebooks)

- Web experiments for Psychologists (overview)
- jsPsych (platform)
- psiTurk (platform)
- oTree (platform)

- Computational cognitive neuroscience lecture (Josh Tenenbaum, MIT)
- Computational Cognitive modeling course (Brenden Lake and Todd Gureckis, NYU)
- Computational modeling book and code (Farell & Lewandowsky)
- Computational modeling workshop by Charley (slides)
- Neuromatch Academy tutorials

- Reinforcement Learning (Sutton & Barto; Sutton’s free online version)

- Neuromatch tutorial on RL
- Reinforcement learning: bringing together computation and cognition (Review paper by Anne Collins)

- Spinning up in Deep RL (OpenAI)
- Deep Mind RL bootcamp (Youtube playlist)

- Kevin P. Murphy Machine Learning: A Probabilistic Perspective
- Kingma & Welling An Introduction to Variational Autoencoders

- Introduction to Graph Theory Douglas B. West

- Graph Representation Learning William L. Hamilton, Mcgill

- Pattern Recognition and Machine Learning Christopher M. Bishop
- Chap8 Graphical Models

- Probabilistic Graphical Models Daphne Koller

- Barbara Sarnecka’s Writing workshop
- Steven Pinker’s - The Sense of Style
- Scientific thinking workshop by Charley (slides)