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teaching:lectures:kga:2018:slides [2019/01/24 12:13]
Hamed Shariat Yazdi adding slides of MLN
teaching:lectures:kga:2018:slides [2019/01/30 09:46] (current)
Hamed Shariat Yazdi adding summary slides
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 ^  Date  ^  Chapter/​Topic ​ ^  Slides ​  ^ ^  Date  ^  Chapter/​Topic ​ ^  Slides ​  ^
-|09.10.2018| 01. Motivation ​ |{{:​teaching:​lectures:​kga:​2018:​01-motivation.pdf|}} | +|09.10.2018| 01. Motivation ​ |{{:​teaching:​lectures:​kga:​2018:​01-motivation.pdf}} | 
-|16.10.2018| 02. Semantic Knowledge Graphs |{{:​teaching:​lectures:​kga:​2018:​02-triplestores.pdf|}}  | +|16.10.2018| 02. Semantic Knowledge Graphs |{{:​teaching:​lectures:​kga:​2018:​02-triplestores.pdf}} ​ | 
-|30.10.2018| 03. Property Graph Databases | {{:​teaching:​lectures:​kga:​2018:​03-propertygraphdatabases.pdf|}}| +|30.10.2018| 03. Property Graph Databases | {{:​teaching:​lectures:​kga:​2018:​03-propertygraphdatabases.pdf}}| 
-|06.11.2018| 04. Statistical Relational Learning | {{:​teaching:​lectures:​kga:​2018:​04-statistcalrelationlearning.pdf|}}| +|06.11.2018| 04. Statistical Relational Learning | {{:​teaching:​lectures:​kga:​2018:​04-statistcalrelationlearning.pdf}}| 
-|13.11.2018| 05. Tensor-Factorization Methods | {{:​teaching:​lectures:​kga:​2018:​05-tensors.pdf|}}| +|13.11.2018| 05. Tensor-Factorization Methods | {{:​teaching:​lectures:​kga:​2018:​05-tensors.pdf}}| 
-|28.11.2018| 06. Stochastic Gradient Descent and Alternating Least Squares ​ | {{:​teaching:​lectures:​kga:​2018:​06-sgd_als.pdf|}}    | +|28.11.2018| 06. Stochastic Gradient Descent and Alternating Least Squares ​ | {{:​teaching:​lectures:​kga:​2018:​06-sgd_als.pdf}} ​   | 
-|04.12.2018| 07. (Feed Forward) Neural Networks | {{:​teaching:​lectures:​kga:​2018:​07-neuralnetworks.pdf|}}  | +|04.12.2018| 07. (Feed Forward) Neural Networks | {{:​teaching:​lectures:​kga:​2018:​07-neuralnetworks.pdf}} ​ | 
-|11.12.2018| 08. Neural Networks for Knowledge Graphs analysis | {{:​teaching:​lectures:​kga:​2018:​08-nnsforkga.pdf|}}  | +|11.12.2018| 08. Neural Networks for Knowledge Graphs analysis | {{:​teaching:​lectures:​kga:​2018:​08-nnsforkga.pdf}} ​ | 
-|18.12.2018| 09. Latent Distance and Graph Feature models | {{:​teaching:​lectures:​kga:​2018:​09-latentdisctancemodels.pdf|}}  | +|18.12.2018| 09. Latent Distance and Graph Feature models | {{:​teaching:​lectures:​kga:​2018:​09-latentdisctancemodels.pdf}} ​ | 
-|15.01.2019| 10. Training Statistical Relational Learning Models | {{:​teaching:​lectures:​kga:​2018:​10-trainingsrlmodels.pdf|}}  | +|15.01.2019| 10. Training Statistical Relational Learning Models | {{:​teaching:​lectures:​kga:​2018:​10-trainingsrlmodels.pdf}} ​ | 
-|22.01.2019| 11. Markov Logic Networks| {{:​teaching:​lectures:​kga:​2018:​11-mlns.pdf|}}  |+|22.01.2019| 11. Markov Logic Networks| {{:​teaching:​lectures:​kga:​2018:​11-mlns.pdf}}  ​
 +|29.01.2019| 12. Summary| {{teaching:​lectures:​kga:​2018:​12-summary.pdf}} ​|
teaching/lectures/kga/2018/slides.txt · Last modified: 2019/01/30 09:46 by Hamed Shariat Yazdi

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