HMC Lab Public Resources
posted on May 15, 2022

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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.

Math

Linear Algebra

• 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 Theory

• 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)

Statistics

• Statistical Methods for Behavioral and Social Sciences (Tobias Gerstenberg, Stanford)
• Statistical Rethinking (Richard McElreath, Max Planck Institute)
  • Youtube playlist
  • R code
• Regression and other stories (Andrew Gelman, Jennifer Hill, Aki Vehtari)
  • https://avehtari.github.io/ROS-Examples/

Programming

Basics

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

Python

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

R

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

Matlab

• Introduction to programming with MATLAB
• MATLAB Cody Challenge

Web programming (for experiments)

• Web experiments for Psychologists (overview)
• jsPsych (platform)
  • Tutorial
  • Demo experiment
• psiTurk (platform)
• oTree (platform)

Neuroscience

• Neuromatch Academy: Computational Neuroscience Tutorials
• Introduction to Computational Neuroscience

Computational Cognitive Science

• 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

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

Human RL

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

Deep RL (with neural networks)

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

Machine Learning

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

Graph Learning

Basic Graph Theory

• Introduction to Graph Theory Douglas B. West

Graph Representation

• Graph Representation Learning William L. Hamilton, Mcgill

Graphical Model

• Pattern Recognition and Machine Learning Christopher M. Bishop
  • Chap8 Graphical Models
• Probabilistic Graphical Models Daphne Koller

Writing

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

PhD planning

• PhD planning template (created by Richard McElreath)