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

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.

Format: 6 half days with lectures and exercises. And reading material in between. The 6 half days will be spread over two weeks. 

Prerequisites: A general background in computer science or a related engineering discipline with modeling experience.

Learning objectives: The objectives of the course is to give the participants a deep understanding of the possibilities and limitations of the UPPAAL tool suite. And to give them hands-on experience with using the tools as well as an understanding of the underlying algorithms.

Organizer: Associate professor Ulrik Nyman, ulrik@cs.aau.dk

Lecturers: Kim G Larsen, Marius Mikučionis, Ulrik Nyman

ECTS: 2

Time: November/December 2019

Place: Selma Lagerlöfs Vej 300

Zip code: 
9220

City: 
Aalborg

Number of seats:

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 5,000 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 three 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 Advances in Design of Interactive Systems


Description: TBD

Format: 4 half days in 2019. The course splits in Part 1 and 2. Part 1 can be followed separately.


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

Lecturers: TBD

ECTS: 2 (Part 1: 1 ECTS)

Time: 

Place: 

Zip code: 

City: 

Number of seats:

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 5,000 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 three 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 Action Research in IT Studies


Description: Action research, design research, design science research are all proactive research approaches with a wide applicability in IT studies. The course will focus primarily on action research and at the same time address the foundational issues for the proactive researcher whether that is an industrial PhD student or a PhD student that has close engagement with the organisations, teams, or other social communities. 

The course will cover:
• Action research, design research, action design research, and design science research in IT: Why? What?
• Organising and managing proactive projects and enacting the action-learning cycles
• Data collection and analysis
• Genres of writing up action/design research studies
• Identifying and presenting contributions
• The utilisation of framework for action and empirical research
• Linkages with engaged scholarship and case study research
• The insider role in action research and reflexivity in general in social studies
This course is particularly relevant for industrial PhD studies.

Prerequisites: The course is specifically addressing PhD students in the application of IT, information systems, and related subjects. PhD students working in other subjects are welcome, but should know that all the example material is drawn from IT studies.

Learning objectives: Know how to organise, conduct, and report from action research.

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

Lecturers: Professor Peter Axel Nielsen, e-mail: pan@cs.aau.dk

ECTS: 2.0

Time: June 3rd-4th, 2019 (9:15-16:30 both days)

Place: Aalborg

Zip code: 

City: 
Aalborg

Number of seats: 20

Deadline: May 13th 2019

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 5,000 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 three 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 Machine learning with probabilistic graphical models

Description: This course gives an introduction to probabilistic graphical models, which play an important role in
modern machine learning and artificial intelligence. Various types of graphical models are introduced, and their use in typical application
domains illustrated (e.g., Hidden Markov Models for biological sequence analysis,
Markov Random Fields for Image Processing). The course emphasizes both modeling and
learning aspects of graphical models. With regard to modeling, we discuss
underlying probabilistic (independence) assumptions that need to be made when choosing  a
particular type of model for a given application domain. Also limitations imposed by
the computational complexity of inference tasks for a given model are considered.
With regard to learning, the most important paradigms for learning graphical models
from data are explained, and exemplified by typical learning tasks.
 
Outline of contents:
 
Basics of probability:
- joint probability distributions
- conditional probability and independence
 
Directed and undirected graphical models
- Bayesian and Markov networks, factor graphs
- Graph structure and independence relations: d-separation and
   the Hammersley-Clifford theorem
 
Latent variable models
- Hidden Markov models
- Unsupervised learning
- template models
- Plate representation
 
Learning and inference
- Structure learning
- Parameter estimation
- Supervised learning
- Bayesian learning
 
Format: Two full days with lectures and one hand-in assignment
Prerequisites: Basic knowledge in probability theory
Learning objectives: Obtain knowledge and application skills about the use of graphical models and methods for probabilistic machine learning 

Organizer: Manfred Jaeger and Thomas Dyhre Nielsen

Lecturers: associate professor Manfred Jaeger, jaeger@cs.aau.dk, and associate professor Thomas Dyhre Nielsen, tdn@cs.aau.dk

ECTS: 2

Time: May 14-15, 2019 (the course dates can be moved to another time in May if needed). 

Place: Selma Lagerløfs vej 300,  Aalborg

Zip code: 
9220

City: 
Aalborg Øst

Number of seats: 30

Deadline: April 23rd, 2019

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 5,000 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 three 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 Exploratory Data Analysis

Description: Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input. An example is a representative of the intended results or, in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind but may not be able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when they are performing a particularly challenging task like finding duplicate items, or when they are simply exploring the data. In this course, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how different data types require different techniques and present algorithms that are specifically designed for relational, textual, and graph data. The course also presents the challenges and new frontiers of machine learning in online settings that have recently attracted the attention of the database community. We conclude with a vision for further research and applications in this area. 

Format:
Readings, lectures, and exercises. 

Prerequisites:
A general background in computer science and general familiarity with database management, as can be achieved through an undergraduate database course, is expected. Participants who have taken a graduate database course will benefit from this additional background.

Learning objectives: 
The goal of this course is to enable the students to  understand ongoing trends in exploratory analysis and example-based methods. In particular, the course will cover techniques designed for relational, textual, and graph data as well as highlight challenges and new frontiers of machine learning in online settings. 

Organizer: professor Katja Hose, khose@cs.aau.dk 

Lecturers: Davide Mottin (Aarhus University)

ECTS: 2

Time: April/May 2019

Place: Aalborg

Zip code: 

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 5,000 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 three 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 in Machine Learning

Description: This course covers one or more selected topics of recent advances in machine learning, e.g., transfer learning OR network representation learning.
 
Topic 1 Transfer learning: A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data labeling efforts.
 
Topic 2 Network representation learning: Larger and larger, more and more sophisticated networks are used in more and more applications. It is well recognized that network data is sophisticated and challenging. To process graph data effectively, the critical challenge is network data representation, that is, how to represent networks properly so that advanced analytic tasks, such as pattern discovery, analysis and prediction, can be conducted efficiently in both time and space. 
 
Format: Lectures
 
Prerequisites: Basic knowledge of machine learning.
 
Learning objectives:
(1) Review the recent progress and achievements on the selected topics.
(2) Understand the specific learning algorithms of the selected topics.
(3) Discuss the major future directions of the selected topics.

Organizer: professor Bin Yang, byang@cs.aau.dk

Lecturers: Sinno Jialin Pan (Nanyang Technological University) OR Peng Cui (Tsinghua University)

ECTS: 2

Time: TBD

Place: Aalborg

Zip code: 

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 5,000 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 three 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 Data Integration and Machine Learning: A Natural Synergy

Description: There is now more data to analyze than ever before. As data volume and variety have increased, so have the ties between machine learning and data integration become stronger. For machine learning to be effective, one must utilize data from the greatest possible variety of sources; and this is why data integration plays a key role. At the same time machine learning is driving automation in data integration, resulting in overall reduction of integration costs and improved accuracy. This PhD course focuses on three aspects of the synergistic relationship between data integration and machine learning: (1) we survey how state-of-the-art data integration solutions rely on machine learning-based approaches for accurate results and effective human-in-the-loop pipelines, (2) we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant data for their analytics exercises, and (3) we discuss open research challenges and opportunities that span across data integration and machine learning.

 
Format: Lectures

Prerequisites: Basics about ML and databases.

Learning objectives: 
The goal of this course is to delineate the interplay between modern data integration techniques and modern machine learning. Specifically, we review (1) how recent advancements in machine learning (such as highly-scalable inference engines and deep learning) are revolutionizing data integration, and (2) how incorporating data integration tasks in machine learning pipelines leads to more accurate and usable systems for analytics. This course will highlight the strong connections between data integration and machine learning, review related technical challenges and recent solutions, and outline open problems that remain to be solved.

Organizer: Associate professor, Hua Lu, luhua@cs.aau.dk

Lecturers: Xin Luna Dong (Amazon)

ECTS: 2

Time: June 27-28, 2019

Place: Aalborg

Zip code: 

City: 
Aalborg

Number of seats: 30

Deadline: June 6th, 2019

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 5,000 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 three 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.