**Machine Learning and the Semantic Web** [Claudia d'Amato](http://www.di.uniba.it/~cdamato) and [Nicola Fanizzi](http://www.di.uniba.it/~fanizzi) [Scuola di Dottorato in Informatica e Matematica](http://dottorato.di.uniba.it) -- Cycles XXXIV-XXXV [Università degli studi di Bari](http://www.uniba.it) "Aldo Moro" (#) Course __Objectives__ Exploring the intersections of _Machine Learning_ with the _Semantic Web_ / _Web of Data_, regarded as a major source for (Big) Data. (#) Lectures (##) Schedule | date | hours | room | |:-----:|:------------:|:-----:| | 26/02 | 9:30 - 13:30 | 2A | | 03/03 | 9:30 - 13:30 | 2A | | 04/03 | 9:30 - 13:30 | 2A | | 06/03 | 9:30 - 13:30 | skype | (##) Content 1. [Introduction](./MLSW-Mod1-Intro.pdf) & [Representation Languages](MLSW-Mod2-Languages.pdf) I * The Semantic Web -- Intro * RDF, RDF-S, OWL, SKOS 2. [Representation Languages](MLSW-Mod2-Languages.pdf) II & [Reasoning](MLSW-Mod3-DLs-Reasoning.pdf) * OWL/DL * Reasoning Services 3. [Introdution to ML](MLSW-Mod4-RelationalLearning.pdf) & [Statistical Learning](MLSW-Mod5-StatLearn.pdf) I: * Relational Learning: Problems and Search Space -- From Parametric to Non Parametric Models * Linear Discrimination, Maximum Margin Models * Kernel Methods & Machines 4. [Statistical Learning](MLSW-Mod5-StatLearn.pdf) II, [ML Methods for SW Problems](MLSW-Mod6-MLforSW.pdf): * Kernels for DLs/OWL, Energy Based Embedding Models (classification+link prediction) * Concept Learning: Ref.Ops, DL-Foil (TDTs) * Inductive retrieval/classification, clustering (TCTs), ontology enrichment, etc. (##) Material + Semantic Web at [W3C](https://www.w3.org/standards/semanticweb/) * [RDF](https://www.w3.org/RDF/), [RDF-Schema](https://www.w3.org/TR/rdf-schema/) * [OWL](https://www.w3.org/TR/owl2-overview/)2 + [Linked Data](http://linkeddata.org/) + [Linked Open Vocabularies](http://lov.okfn.org/dataset/lov/) + Machine Learning textbooks (for further reading/insights) + DeRaedt: [Logical and Relational Learning](https://link.springer.com/book/10.1007/978-3-540-68856-3). Springer + Mitchell: [Machine Learning](http://www.cs.cmu.edu/~tom/mlbook.html). McGraw-Hill + Hastie et al.: [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/Papers/ESLII.pdf). 2e. Springer + Alpaydin: [Introduction to Machine Learning](https://mitpress.mit.edu/books/introduction-machine-learning-fourth-edition). 4e. MIT Press (#) Exam Presentation of a (draft of a) solution to an assigned problem of student's choice. [List](MLSW-Projects.pdf) of proposed problems. ----  --- (#) Previous Editions [[2018](index-2018.html)]