## 2016

**Lecturers**: Gergő Orbán, Mihály 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**

- Slides, em_hf.txt
- Probabilistic programming languages
- Stan
- C. M. Bishop: Pattern Recognition and Machine learning – Mixture Models and EM

**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

- Slides
- Videolecture about probabilistic graphical models by Sam Roweis
- C. M. Bishop: Pattern Recognition and Machine learning – intro to probability theory, directed graphical models
- MOOC about probabilistic graphical models by Daphne Koller, sections Introduction and Bayesian Network fundamentals are relevant

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

- Slides
- D. MacKay: Information Theory, Inference, and Learning Algorithms – inverse probability, entropy
- T. L. Griffiths, C. Kemp, J. B. Tenenbaum: Bayesian models of cognition- sections 1,2

**Bayesian behaviour** – MG, 2 Mar

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

- Slides,
- Video Lectures by David MacKay 1, 2
- Hamiltonian Monte Carlo book chapter

**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

- Slides, em_hf.txt
- C. M. Bishop: Pattern Recognition and Machine learning – Mixture Models and EM

**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