Summer Schools

Bayesian Cognition 1

Bayesian Cognition II

Introduction to graphical models and probabilistic programs I

Introduction to graphical models and probabilistic programs II

Bayesian vision

Bayesian machine learning

Introduction to Markov Chain Monte Carlo

Foundational models of language I

Foundational models of language II

Other inference techniques

Approximate Inference

Bayesian model of information accumulation in choice-reaction time tasks

Advanced Markov Chain Monte Carlo

Deep Networks

Human learning to learn via hierarchical models

Machine learning and advanced hierarchical models

BLOG

Markov logic

More Markov logic

Church (and the Probabilistic Language of Thought)

Reasoning with Causes and Counterfactuals

Computation and Representation in Brains: A few Observations and one Speculation

Representing Relationships: Conditional Modeling and Sufficiency

Context versus Computation

Human sensory systems as perceptual inference

Probabilistic models of motion perception

The computational magic of the ventral stream: towards a theory

Probability models for vision with known graph structure I & II

Compositional learning for vision

Decision making and motor control

Learning And-Or Graph Representations for Objects and Events

Top-down/Bottom-up Inference in And-Or graphs

Intuitive Physics in the context of visual scene understanding

Non-parametric language models I

Non-parametric language models II

Sentence processing: Surprisal and noisy channel models

Inducing Semantics

Fragment Grammars: Productivity and reuse in language

Learning syntax

Probabilistic Pragmatics (and Semantics)

Language use, eye movements

Models of phonology

Predicate logic and human learning

Kinship

Bayesian theory of mind

Learning structured concepts from structured minds

Learning abstract structural forms, learning about social systems

Human causal induction

Markov Chain Monte Carlo with People

Rational Process Models