Software Engineering for Smart Data Analytics & Smart Data Analytics for Software Engineering

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KGA Slides

Here you find an outline of the lecture (order and topics might be adopted during the semester). Slides form last year are linked and will be updated shortly before the upcoming class. This page contains lecture material from last year, which will updated during the course.
Date Chapter/Topic Slides (and date of last update)
10.10.17 01. Motivation 01-motivation.pdf
25.11.17 02. RDF Databases 02-rdfdatabases.pdf
31.10.17 - public holiday -
07.11.17 03. Property Graph Databases 03-propertygraphdatabases.pdf
14.11.17 04. Statistical relational learning 04-statistcalrelationlearning.pdf
21.11.17 05. Tensor-Factorization methods 05-tensors.pdf
28.11.17 06. Alternating Least Squares and Stochastic Gradient Descent 06-sgd_als.pdf
12.12.17 07. Introducing Neural Networks 07-neuralnetworks.pdf
19.12.17 08. Neural Networks for KGA 08-nnsforkga.pdf
09.12.17 09. Latent Distance and Graph Feature models 09-ldandgfmodels.pdf
16.01.18 10. Training SRL Models 10-trainingsrlmodels.pdf
23.01.18 11. Markov Logic Networks 11-mlns.pdf
30.01.18 12. Summary 12-summary.pdf

The Slides of the talk of our invited speaker Paul Groth can be found here:

Date Chapter/Topic Slides (and date of last update)
07.02.17 Knowledge Graph Analysis in Practice kg-analysis-elsevier-feb82017.pptx
teaching/lectures/kga/2017/slides.1525831170.txt · Last modified: 2018/05/09 01:59 by

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