Instructor
Dr. Charley Wu (charley[dot]wu[at]uni-tuebingen[dot]de)
Teaching Assistant
TBD
General Information
Location: TBD
Lecture: TBD
Tutorial: TBD
Office Hours: Charley (after the lecture)
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.
Grading
Schedule
Lecture: TBD
Tutorial: TBD
Note that readings are still subject to change
| Date | Remarks | Readings | Lecture | Lecturer | TA |
|---|---|---|---|---|---|
| Week 1: | Spicer & Sanborn (2019). What does the mind learn? | Introduction | Wu | TBD | |
| Week 2: | [1] Behaviorism [2] What is a perceptron? (Blog post) | Origins of biological and artificial learning | Wu | TBD | |
| Week 3: | Wikenheiser & Schoenbaum (2016) | Symbolic AI and Cognitive maps | Wu | TBD | |
| Week 4: | Dezfouli & Balleine (2012) | Introduction to RL | Wu | TBD | |
| Week 5: | Botvinick, Niv, & Barto (2009) | Advances in RL | Wu | TBD | |
| Week 6: | Murphy (2023) | Concepts and Categories | Wu | TBD | |
| Week 7: | Varma & Prasad | Supervised and Unsupervised learning | Wu | TBD | |
| Week 8: | Wu, Meder, & Schulz (in prep); sent via email | Function learning | Wu | TBD | |
| Week 9: | Botvinick et al., 2020 | Common tools for understanding brains and neural networks | Wu | TBD | |
| Week 10: | Landauer & Dumais (1997) | Language and semantics | Wu | TBD | |
| Week 11: | Gershman (2023) | General Principles | Wu | Wu | |
| Exam 1 | TBD | ||||
| Exam 2 | TBD |