Teaching
I will teach Bayesian models & data analysis (CGS698C) next semester. The course will be open for PG and UG students at IIT Kanpur. The syllabus and the course material will be available here.
Current courses
Past courses
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