2016

Lecturers: Gergő OrbánMihály Bányai, Merse E. Gáspár and Dávid Nagy

Introduction. Computational approach, perception as inference, representation, coding, why probabilities?  – MB, 8 Feb

 

Knowledge representation. Formal systems, logic, probability theory – DN, 15 Feb

 

Probabilistic models. Probability calculus, graphical models, Bayesian inference, approximate inference – DN, 22 Feb

 

Bayesian behaviour – MG, 7 Mar

 

Approximate inference I. Iterative estimation, mixture distributions, EM – MB, 16 Mar – 18:00,  room 0-817
Approximate inference II: Sampling. MCMC – MG, 21 Mar
Measuring priors – GO, 4 Apr
Neural representation of probabilities. PPC, sampling hypothesis – MG, 11 Apr
Bayesian modelling of vision I. PCA, the Olshausen & Field model,  Modelling correlations of filters, GSM – GO, 18 Apr
Bayesian modelling of vision II. Higher-level vision – GO, 25 Apr
Structure learning. Learning theory, automatic Occam’s razor, visual chunk learning – DN, 2 May
Decision making and reinforcement learning – MB, 9 May

 


 


 

 

2015

Introduction. Computational approach, perception as inference, representation, coding, why probabilities?  – MB, 9 Feb

 

Probabilistic models I. Knowledge representation, probability theory, graphical models – MB, 16 Feb

 

Probabilistic models II: Inference. Model inversion, density estimation, ML, MAP, approximate inference – DN, 23 feb

 

Bayesian behaviour – MG, 2 Mar

 

Probabilistic models III: Sampling. MCMC – MG, 9 Mar

 

Identification of priors – GO, 16 Mar

 

Bayesian modelling of vision I. Linear-Gaussian models, PCA, the Olshausen & Field model – GO, 23 Mar

 

Probabilistic models IV. Iterative parameter estimation ,mixture distributions, EM – MB, 30 Mar

 

Neural representation of probabilities. PPC, sampling hypothesis – MG, 13 Apr

 

Bayesian modelling of vision II. Modelling correlations of filters, GSM – MB, 20 Apr

 

Models of higher-level vision. Texture representation, slow feature analysis – GO, 27 Apr

 

Structure learning. Formal learning theory, automatic Occam’s razor, visual chunk learning – DN, 4 May