Welcome to From Timed Automata to Stochastic Hybrid Games - Model Checking, Synthesis, Refinement, Performance Analysis and Machine Learning (2021)

Description: Timed automata and games, priced timed automata and energy automata have emerged as useful formalisms for modeling real-time and energy-aware systems as found in several embedded and cyber-physical systems. During the last 20 years the real-time model checker UPPAAL has been developed allowing for efficient verification of hard timing constraints of timed automata. Moreover a number of significant branches exists, e.g. UPPAAL CORA providing efficient support for optimization, and UPPAAL TIGA allowing for automatic synthesis of strategies for given safety and liveness objectives, and ECDAR supports refinement and compositional development of real-time systems. Also the branch UPPAAL SMC, provides a highly scalable new engine supporting (distributed) statistical model checking of stochastic hybrid automata, and most recently the new branch UPPAAL STRATEGO supporting safe and optimal strategies for stochastic hybrid games by combining symbolic methods with machine learning. The course will review the various branches of UPPAAL, the corresponding modeling formalisms as well as the symbolic or statistical algorithms applied. Also, examples on applications of the tools suite will be given to a range of real-time and cyber-physical examples including schedulability and performance evaluation of mixed criticality systems, modeling and analysis of biological systems, energy-aware wireless sensor networks, smart grids and smart houses and battery scheduling.

Organizer: Associate Professor Ulrik Nyman - ulrik@cs.aau.dk

Lecturers: Kim G. Larsen, Ulrik Nyman, Marius Mikučionis

ECTS: 2.0

Time: November/December 2021

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats: 30

Deadline: 


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.


Welcome to RDF Graph Summarizatin: Principles, Techniques and Applications (2021)


Description: .

RDF is a popular model to represent Knowledge Graphs and Linked Open Data. The explosion in the amount of the RDF data on the Web has led to the need to explore, query, and understand such data sources. The task is challenging due to the complex and heterogeneous structure of RDF graphs which, unlike relational databases, do not come with a structure-dictating schema. Hence, summarization has been applied to RDF data to facilitate these tasks by extracting concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; the summarization goal, and the main computational tools employed for summarizing graphs, are the main factors behind this diversity. 

This course presents a structured analysis and comparison of existing work in the area of RDF summarization. The concepts at the core of these approaches will be presented, and their main technical aspects and implementations as well as their use cases and shortcomings will be discussed.  

Learning goals cover four main parts: 

  1. Semantic summaries applications: The first part will introduce preliminaries and deal with the main classes of application contexts that have justified the need for RDF summaries such as indexing, estimating the size of query results, source selection, graph visualization and schema discovery.
  2. Structural summarization methods: Then, the methods and techniques for summarizing semantic graphs (based mostly on the graph structure, i.e., the paths and sub-graphs available in the RDF graph) will be presented and explained.
  3. Pattern mining methods: This part covers methods that employ mining techniques to identify patterns appearing in the semantic graph. A pattern might be a set of instances having a certain set of properties, which are in exact or approximate terms representative of the graph or provide enough information on the graph using some cost function.
  4. Statistical methods: Finally, we will discuss other techniques that try to qualitatively summarize the contents of a graph by counting occurrences, building histograms, measuring frequencies and other statistical measures based on the available semantic graph.

The final evaluation will be based on exercises on the various techniques and/or a mini-project for implementing such a summarization technique. 

Organizer: Professor Katja Hose - khose@cs.aau.dk

Lecturers: Haridimos Kondylakis

ECTS: 2.0

Time: June 2021

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats:20 

Deadline: 01 May 2021


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
Welcome to Digital Transformation (2021)


Description: The course will address one of the most topical issues of our time, namely the widespread digitalisation of society, businesses, and public agencies. When digitalisation becomes even more radical change it gets referred to as digital transformation. In this course we will cover this through online lectures and interactive sessions with the lecturer and we will exercise the lectured material through group work. Based on this the participants will report on their learning in a small report (argumentative essay). The topics that will be covered are among others: 

  • What is digital transformation?
  • Platformatisation and why it's a key driver in digitalisation?
  • What is blockchain? Why is it important in digitalisation?
  • Internet of things as a current technological trend, and how it becomes useful through artificial intelligence
  • How can digital transformation be conducted?

Organizer: Professor Peter Axel Nielsen - pan@cs.aau.dk

Lecturers: Associate Professor Carsten Sørensen.

ECTS: 2.0

Time: May 2021

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats: 20

Deadline:


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
Welcome to Advanced Topics of Machine Learning: Neural Architecture Search (2021)


Description: The Advanced Topics in Machine Learning course covers one or more selected topics of recent advances in machine learning. In particular, this year the course covers neural architecture search. After this course, you will be able to have the knowledge on 1. Underlying mathematical and algorithmic principles of neural architecture search. 2. The key factors that have made neural architecture search successful for some application domains. 

Organizer: Professor Bin Yang - byang@cs.aau.dk

Lecturers:

ECTS: 2.0

Time: May 2021

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats: 30

Deadline:


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
Welcome to Graph Filters for Processing and Learning Data over Graphs (2021)


Description: Data generated by networks are almost everywhere such as data generated by social, biological, or sensor networks, to name just a few. Network data are particular because they are complex and irregular, therefore, do not lend themselves to standard processing tools. Recent years have seen a surge of interest to extend signal processing and machine learning concepts to network data. In this course, we will cover the fundamental concepts to process and learn from data over networks. In particular, we will discuss concepts from graph signal processing, graph neural networks, and detail their role for a series of network data problems such authorship attribution, recommender systems, and multivariate time-series forecasting.

Specific topics include: the graph Fourier transform, regularisation over graphs, graph filters. models for time-varying processes over graphs, graph neural networks, edge varying graph neural networks (EdgeNets), graph-adaptive activation functions, graph-time convolutional networks, and graph dropout.

After this course, you will have knowledge on:

1. The underlying mathematical principles about processing and learning from data over networks.
2. Graph neural networks and different architectures.

3. Applications of network data processing tasks.

The course is organised in the form of lectures with working group exercises and interactive discussions. Students attending this course need to have basic knowledge of linear algebra, machine learning, and deep learning. Evaluation methods include a exam of 30 minutes.



Organizer: Associate Professor Chenjuan Guo - cguo@cs.aau.dk

Lecturers: Assistant Professor Elvin Isufi - E.Isufi-1@tudelft.nl

ECTS: 2.0

Time: 31 May - 01 June 2021

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats: 60

Deadline: 10 May 2021


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
Welcome to Automated Planning Tools for Intelligent Decision Making (2021)


Description: Planning deals with intelligent decision making to decide which actions to perform and how to schedule them during execution. This includes a broad class of problems that has all kinds of applications in different fields. Some examples include scheduling jobs in factory control, finding sequences of chemistry reactions, discourse planning in natural language generation, finding weaknesses in network security, etc. Despite these applications look completely different from each other, they can be solved with general planning tools that anyone can apply to their own problems. 

This course introduces several planning tools to a broad audience so that in the future experts on different areas can apply them in their own field of expertise. We cover three different perspectives: AI planning, model checking, and operational research. 

Students will learn the basics of how each area models planning problems. There will be hands-on sessions where students will familiarize themselves with the tools and will apply them to solve some exercises, possibly related to their own areas.

Organizer:  Associate Professor Alvaro Torralba - alto@cs.aau.dk

Lecturers: Assistant Professor Peter Gjøl Jensen - pgj@cs.aau.dk, Professor Kim Guldstrand Larsen - kgl@cs.aau.dk, Assistant Professor Inkyung Sung - inkyung_sung@mp.aau.dk, Associate Professor Alvaro Torralba - alto@cs.aau.dk, Peter Nielsen 

ECTS: 3.0

Time: April 27, 29 and May 4, 6, 11 and 20. 

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats: 50

Deadline: 06 April 2021


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
Welcome to Constructive and Explainable Machine Learning (2021)


Description: The course will cover a set of problems and techniques related to learning in structured domains, touching upon topics like 

structured-output learning, statistical relational learning, learning with constraints, interactive machine learning and explainable AI. 

The course will consist of frontal lessons combined with hands-on exercises.  Students will be asked to complete a homework assignment to get the credits for the course.

Organizer: Manfred Jaeger

Lecturers: Andrea Passerini

ECTS: 2.0

Time: 17-21 May 2021

Place: TBA

Zip code: 
TBA

City: TBA

Number of seats: 30

Deadline: 26 April 2021


Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.
Welcome to Time Series Modeling and Analytics (2021)



Description: The course will cover concepts, methods, algorithms and tools for time series management, including modeling, representation, mining, and analytics. First, the course will cover time series representation, e.g., sampling, approximation, symbolic representations such as piece-wise constant models, principal component analysis (PCA), and perceptually important points (PIP).  Second, the course will cover time series analytics, e.g., pattern discovery/mining, rule discovery, time series classification, motif discovery, and time series summarization.

Organizer: Professor Torben Bach Pedersen 

Lecturers: Professor Themis Palpanis

ECTS: 2.0

Time: Summer 2021

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats: 20

Deadline: 

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.