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

 
Attached files: 
application/pdf iconL1: Introduction
application/pdf iconL2: Naive Bayesian networks
application/pdf iconL3: Full Bayesian inference using NBNs
application/xml iconCovid-19.xml
application/xml iconGimnáziumi tantárgyi háló BN
application/pdf iconL4: Hidden Markov Models
application/pdf iconL5-E2: Hidden Markov Models labor
application/pdf iconL6: Applications and extensions of Hidden Markov Models
application/pdf iconL6+: Grammars & automata
application/pdf iconL7: Parameter learning in HMMs
application/pdf iconL9: Decision networks, value of further information
application/xml iconL9: "COVID-19 home quarantine" decision network
application/pdf iconL10: Bayesian networks and extensions: canonical models, dynamic BNs
application/pdf iconL10++: Updated version of L10 lecture (+context-specific independencies and decision trees)
application/pdf iconL10++: whiteboard: page 1
application/pdf iconL11-12: Towards causal inference
application/pdf iconL13: Causal inference (exercise on April 7,Tuesday)
application/xml iconSprinkler: causal
application/xml iconSprinkler: contrafactual
application/pdf iconL13-L14: Homework description and guide
application/pdf iconL15: The pomegranate python library Bayesian networks (colab)
application/xml iconAdvanced driver assistance system (ADAS): A decision network for driver's fatigue with decision tree local models
application/xml iconADAS for colab: a simplified decision network modeling driver's fatigue
application/pdf iconL15++: Software and programming environments for PGMs/BNs
application/pdf iconL16: Exact inference in polytree Bayesian networks
application/pdf iconL17: Stochastic inference in Bayesian networks: Gibbs/MCMC intro
application/pdf iconBeyond/below simple decisions: extra online materials
application/pdf iconL18a: Practical exact inference in general BNs (PPTC)
application/pdf iconL18b: Practical stochastic inference using Markov Chain Monte Carlo
application/pdf icon++: Szepesvári Csaba: The explore-exploit dilemma
application/pdf iconL19: Full Bayesian learning and Probably Approximately Correct Learning
application/pdf iconL19: Bayesian network learning
application/pdf iconLast lecture: results, trends, topics
Submitted by Antal Péter on 2015. February 11. 10:46 | Last updated: 2020. May 19. 07:11
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