Courses

Courses of the 1st semester

Machine Learning (DWS101)

Learning Outcomes
Students become familiar with the main concepts as well as with specific data analysis and machine learning techniques and become also familiar with many applications of it. They also acquire skills in reviewing scientific papers, giving scientific presentations and working in practice with data using various algorithms and relevant machine learning software.

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Advance free, creative and causative thinking

Course Content (Syllabus)
Introduction, Regression, Decision Trees, Rule Learning, Instance – Based Learning, Bayesian Learning, Learning with Genetic Algorithms, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Text Mining, Machine Learning Software (Python).

Technologies for Big Data Analytics (DWS102)

Learning Outcomes
1. Students will get important knowledge in big data management and analytics
2. They will work in teams
3. They will be more confident by presenting their work in class
4. They will get in contact with modern big data analytics techniques with a lot of applications in Industry.

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
  • Work in teams
  • Generate new research ideas

Course Content (Syllabus)
– Introduction to Big Data Management and Analytics
– Hadoop: basic and advanved topics
– The Hadoop ecosystem: HDFS, Hbase, Pig, Hive
– NoSQL databases
– Theoretical issues in MapReduce
– The Scala programming language
– The Spark platform: basic and advanced issues
– Streaming, SQL, Machine Learning, GraphΧ: the basic libraries
– Data exploration using SparkR
– Algorithm design in Spark
– Graph databases
– Other systems: Giraph, GraphLab, Hama, BlinlkDB

Semantic Web (DWS103)

Learning Outcomes

Knowledge: Familiarization with principles and technologies for representing and reasoning about data, metadata, and knowledge in the Semantic Web, Familiarization with Ontology Engineering techniques, Training on XML editors/processors and Ontology editors.

Skills: Developing metadata vocabularies and ontologies, Representation of data, metadata, knowledge and ontologies using the following languages: XML, DTD, XML Schema, XSLT, XPATH, RDF, RDF Schema, SPARQL, OWL and SWRL.

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Generate new research ideas
  • Be critical and self-critical
  • Advance free, creative and causative thinking

Course Content (Syllabus)
Introduction and General vision of the Semantic Web (SW). SW Architecture. Technologies and Languages of the SW. XML (Description, DTD, XML Schema, Namespaces, XPath, XSLT, XML tools). RDF (Description, XML syntax, RDF Schema, RDF/RDFS Semantics, Querying RDF/RDFS with SPARQL, Linked Open Data, RDF tools). OWL (Introduction to ontologies and OWL, Description and syntax, OWL flavors, Examples, OWL in OWL, Future extensions, OWL tools). OWL2 Presentation. Ontology Engineering (Ontology creation, Reusing ontologies, Semi – automatic methods). SW Applications. Linked Open Data. Logic and Inferencing (SWRL, OWL2 RL, RIF, RuleML).

Text Mining and Natural Language Processing (DWS104)

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Generate new research ideas
  • Be critical and self-critical
  • Advance free, creative and causative thinking

Course Content (Syllabus)
Text Processing, Language Modeling with N-Grams, Text Classifiers, Vector Semantics, Neural Nets and Neural Language Models, Part-of-speech Tagging, Sequence Processing with Recurrent Networks, Information Extraction, Question Answering, Dialog Systems and Chatbots (Conversational Agents)

Social Network Analysis (DWS105)

Learning Outcomes
1. Identification of social networks and related analysis methods
2. Construction and analysis tools for data that are not in network form (text, image, etc.)
3. Learning of important properties and process characteristics on networks like community detection, sentiment analysis, information diffusion, network robustness, etc.

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Generate new research ideas
  • Be critical and self-critical
  • Advance free, creative and causative thinking

Course Content (Syllabus)
Centrality measures, network models (random) and characteristics (small world phenomena, power law distribution on various networks characteristics), mechanisms for network generation (preferential attachment), network robustness. Online social data sources, community detection, sentiment analytics basics, information diffusion, influence detection, fraud detection.

Courses of the 2nd semester

Web Mining (DWS201)

Learning Outcomes
1. Identify data structures and models World Wide Web
2. Construct and analyze Web data models and understand data relevance metrics i
3. Learning important Web properties, attributes and algorithms to find and recognize common patterns, behaviors, and Web clusters

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Work in teams
  • Generate new research ideas
  • Appreciate diversity and multiculturality
  • Demonstrate social, professional and ethical commitment and sensitivity to gender issues
  • Be critical and self-critical
  • Advance free, creative and causative thinking

Course Content (Syllabus)
• Introduction to the basic concepts of information and data management within World Wide Web.
• Data types on the web and representation.
• World Wide Web Structure and Graph Model.
• World Wide Web Performance Metrics and Information Sharing Techniques.
• Information aggregation techniques in Social Networks.
• Sentiment analysis and behavioral analytics on the Web.
• Recommendations in Social Media and New Collaborative Web Environments.

Distributed Data Processing (DWS202)

Learning Outcomes
Training on distributed data management topics. Acquisition of practical and theoretical knowledge of through the attendance of lectures and the implementation of projects.

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an international context
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking

Course Content (Syllabus)
Basic concepts of Distributed Databases (DDB), Distributed Database Systems, DDB Architecture, Autonomy and Heterogeneity Issues, Design of DDBs and Data Allocation, Distributed query processing and optimization, Distributed Database Transactions, reliability and data replication, Parallel databases and relationship with DDBs, Grid data management, Adaptive query processing, Map-Reduce techniques, Modern systems, NoSQL DBMSs and current research issues.

Mining from Massive Datasets (DWS203)

Learning Outcomes
1. Students will get important knowledge in big data management and analytics
2. They will work in teams
3. They will be more confident by presenting their work in class
4. They will get in contact with modern big data analytics techniques with a lot of applications in Industry.General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
  • Work in teams
  • Generate new research ideas

Course Content (Syllabus)
– Introduction to Big Data Management and Analytics
– Hadoop: basic and advanved topics
– The Hadoop ecosystem: HDFS, Hbase, Pig, Hive
– NoSQL databases
– Theoretical issues in MapReduce
– The Scala programming language
– The Spark platform: basic and advanced issues
– Streaming, SQL, Machine Learning, GraphΧ: the basic libraries
– Data exploration using SparkR
– Algorithm design in Spark
– Graph databases
– Other systems: Giraph, GraphLab, Hama, BlinlkDB

Decentralized Technologies (DWS204)

Learning Outcomes
Understanding decentralized Systems principles, identify core issues involved in Internet decentralization, the models od information representation on blockchains. -based applications and Web transactions. Training on designing and developing of blockchain centered applications with special focus on the economic models as use cases. Emphasis on applications which are relevant with blockchain analysis. Emphasis on hands-on and co-working experience.

General Competences

  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Work autonomously
  • Work in teams
  • Generate new research ideas
  • Design and manage projects
  • Demonstrate social, professional and ethical commitment and sensitivity to gender issues
  • Be critical and self-critical
  • Advance free, creative and causative thinking

Course Content (Syllabus)
Introduction to decentralized technologies. Classification of decentralized ledger technologies. Emphasis on privacy and security aspects of decentralization. Introduction to Blockchain Technology. Introduction to Bitcoin. Scripting, Etherium, Hyperledger implementations.

Advanced Topics in Machine Learning (DWS205)

The description is under construction!

Advanced Topics in Databases (DWS206)

Learning Outcomes
After the course completion students will have a concrete view of the methods used for spatial and multimedia data mamagement. Moreover, they will be able to apply indexing schemes (as well as other methods) in multimedia data. In addition, the assignments will help students to gain additional knowledge and solve practical problems.

General Competences

  • Apply knowledge in practice
  • Make decisions
  • Work autonomously
  • Generate new research ideas

Course Content (Syllabus)
Spatial and Multimedia Databases with emphasis on high dimensional data, Spatial Data Models, Spatial Query Languages, Spatial Storage and Indexing, Spatial Query Processing Algorithms and Optimization, Spatial Networks, Information Retrieval, Content-based Information Systems, Multidimensional Indexing Techniques, B-Trees and their variants, persistent trees, buffer trees, R-Trees and their variants, X-Trees, M-Trees, Slim-Trees, Similarity Query Processing Algorithms in Multidimensional Spaces, Similarity Query Processing Algorithms in Metric Spaces, Preference Queries (top-k, skylines), hash indexing techniques.