General Principles of Human and Machine Learning
posted on March 2, 2023


Dr. Charley Wu (charley[dot]wu[at]uni-tuebingen[dot]de)
Dr. Charline Tessereau (charline[dot]tessereau[at]tuebingen[dot]mpg[dot]de)

Teaching Assistant

Hanqi Zhou (hanqi[dot]zhou[at]uni-tuebingen[dot]de)
Turan Orujlu (turan[dot]orujlu[at]uni-tuebingen[dot]de)
Mani Hamidi (manihamidi[at]gmail[dot]come)

General Information

Location: Ground floor seminar room, AI building, Maria-von-Linden-Str. 6, D-72076 Tübingen
Lecture: Thursdays from 10:00 - 12:00
Tutorial: Friday from 16:00 - 18:00
Office Hours: TBD

Course description and prerequisites:

The goal of the course is to teach modern approaches to machine learning in parallel with the science of human learning. Theories of human learning have directly inspired algorithms for training artificial agents, while advances in AI have provided novel insights into the nature of human intelligence. Machine learning algorithms are increasingly reaching or exceeding human-level expertise. Yet, they can still be fooled by rather trivial tasks that any human could solve. How have these two fields separately approached the study of artificial and biological intelligence? What are general principles of learning that are shared across human and machine learners?

The course covers how both biological and artificial systems i) learn from experience (Reinforcement Learning), ii) learn about concepts and categories, and ii) learn functions and programs. Each week, there will be one lecture and one tutorial session. No prerequisites are required, but some basic familiarity with linear algebra and basic mathematical notation will be necessary.



Lecture: Thursdays from 10:00 - 12:00
Tutorial: Friday from 16:00 - 18:00

Date Remarks Readings Lecture Lecturer TA
Week 1: April 20,21   Spicer & Sanborn (2019). What does the mind learn? doi:10.1016/j.conb.2019.02.004 Introduction Wu Hamidi
Week 2: April 27, 28   [1] Behaviorism [2] What is a perceptron? (Blog post) Origins of biological and artificial learning Wu Orujlu
Week 3: May 4, 5   Wikenheiser & Schoenbaum (2016) Cognitive maps Wu Hamidi
Week 4: May 11,12   TBD Introduction to RL Tessereau Zhou
Week 5: May 19 Christihimmelfahrt NA No lecture   No tutorial
Week 6: May 25, 26   TBD Advances in RL Tessereau Orujlu
Week 7: No classes Pfinstpause NA No lecture   No Tutorial
Week 8: June 9 Fronleichnam NA No lecture   No Tutorial
Week 9: June 15,16   Murphy (2023) Concepts and Categories Wu Hamidi
Week 10: June 22,23   TBD Supervised and Unsupervised learning Wu Zhou
Week 11: June 29,30   Tenenbaum & Griffiths (2001) Generalization Wu Hamidi
Week 12: July 6,7   TBD Common tools for understanding brains and neural networks Tessereau Orujlu
Week 13: July 13,14   TBD Language and semantics Wu Zhou
Week 14: July 20,21   TBD General Principles Wu & Tessereau Wu
TBD Exams