Teaching

I have taught the following courses at the University of Potsdam. These courses were open to MSc students from cognitive science, cognitive systems, computer science, embodied cognition, linguistics, and psychology programmes. 

Bayesian statistical inference 1 (winter semester)

Program modules: CSE-MA-014, AM21, AM22, IECL-MA-41

Prerequisites: R/Python, basic mathematics

Topics

• Sets and probability

• Random variables

• Bayes' theorem

• Model building

• Parameter estimation: Conjugate priors

• Parameter estimation: Markov chain Monte Carlo, Hamiltonian Monte Carlo

• Bayesian linear modeling

• Hierarchical modeling

• Meta-analysis

• Model evaluation: Cross-validation, Bayes factors

• Theory testing

Foundations of Mathematics (winter semester)

Program modules: FM1

Prerequisites: None

Topics

• Functions

• Differentiation

• Matrix algebra

• Vector algebra

• Integration

• Multivariable calculus

• Sets and probability

• Random variables

Bayesian statistical inference 2 (summer semester)

Program modules: LIN-MS-024, MAT-DSAM2B, IECL-MA-02, CSE-MA-014, AM21, AM22 

Prerequisite: Bayesian statistical inference 1

Topics

• Introduction to Stan, a probabilistic programming language

• Linear modeling in Stan

• Reparametrization

• Model comparison

• Multinomial processing trees

• Finite mixture models

• Lognormal race models

• Individual difference models

Sentence processing theories (summer semester)

Program modules: LIN-MS-024

Prerequisites: None

Topics

• A brief history of sentence processing theories

• Theories based on working memory constraints

• Theories based on resource allocation and information-theoretic principles

• Representation distortion-based theories

• Connectionism

• Self-organized parsing