Előadások

A keddi órák Teams-ben: link
 
A kéthetenkénti szerdai órák Teams-ben: link
 
A távoktatáson kívül a normál ügymenet marad, azaz HF portál HF-knek, kurzus honlap anyagoknak (ide igyekszem előre feltenni majd az előadásanyagokat és az azokhoz tartozó ajánlott olvasmányokat).
 
L22 (last lecture): Feedback for homeworks + Trends/Related courses
 
L21: Consultation
 
L19-L20: Structure and parameter learning of Bayesian networks
 
 
 
 
L18: Exact and stochastic inference in BNs in practice
 
L17: Stochastic inference in Bayesian networks: Gibbs/MCMC intro
 

L15 (április 15.): Programozási környezetek és szoftverrendszerek Bayes hálókhoz (háttéranyag: pomegranate, labor: colab, BayesCube mode: Advanced driver assistance system (ADAS) model, simplified colab ADAS model) 

 
L14: A nagy HF részleteinek megbeszélése.

L11-L12 - L13:

L4-7: AIMA háttéranyag:

15. fejezet

 

L2-3: ajánlott olvasmány:

13. fejezet - Bizonytalanság: 13.2-13.3

L1: ajánlott olvasmány (előzmény/háttér/anyag):

13. fejezet - Bizonytalanság: 13.1

 
Csatolt fájlok: 
application/pdf ikonL1: Introduction
application/pdf ikonL2: Naive Bayesian networks
application/pdf ikonL3: Full Bayesian inference using NBNs
application/xml ikonCovid-19.xml
application/xml ikonGimnáziumi tantárgyi háló BN
application/pdf ikonL4: Hidden Markov Models
application/pdf ikonL5-E2: Hidden Markov Models labor
application/pdf ikonL6: Applications and extensions of Hidden Markov Models
application/pdf ikonL6+: Grammars & automata
application/pdf ikonL7: Parameter learning in HMMs
application/pdf ikonL9: Decision networks, value of further information
application/xml ikonL9: "COVID-19 home quarantine" decision network
application/pdf ikonL10: Bayesian networks and extensions: canonical models, dynamic BNs
application/pdf ikonL10++: Updated version of L10 lecture (+context-specific independencies and decision trees)
application/pdf ikonL10++: whiteboard: page 1
application/pdf ikonL11-12: Towards causal inference
application/pdf ikonL13: Causal inference (exercise on April 7,Tuesday)
application/xml ikonSprinkler: causal
application/xml ikonSprinkler: contrafactual
application/pdf ikonL13-L14: Homework description and guide
application/pdf ikonL15: The pomegranate python library Bayesian networks (colab)
application/xml ikonAdvanced driver assistance system (ADAS): A decision network for driver's fatigue with decision tree local models
application/xml ikonADAS for colab: a simplified decision network modeling driver's fatigue
application/pdf ikonL15++: Software and programming environments for PGMs/BNs
application/pdf ikonL16: Exact inference in polytree Bayesian networks
application/pdf ikonL17: Stochastic inference in Bayesian networks: Gibbs/MCMC intro
application/pdf ikonBeyond/below simple decisions: extra online materials
application/pdf ikonL18a: Practical exact inference in general BNs (PPTC)
application/pdf ikonL18b: Practical stochastic inference using Markov Chain Monte Carlo
application/pdf ikon++: Szepesvári Csaba: The explore-exploit dilemma
application/pdf ikonL19: Full Bayesian learning and Probably Approximately Correct Learning
application/pdf ikonL19: Bayesian network learning
application/pdf ikonLast lecture: results, trends, topics
Antal Péter, 2015. február 11. 11:46 | Legutóbb frissítve: 2020. május 19. 08:11
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