Courses

[TU Graz > Current Lectures – Aktuelle Lehrveranstaltungen]


185.A83 Machine Learning for Health Informatics (class of 2019) Start: 5th March 2019
185.A83 Machine Learning for Health Informatics (class of 2019) Start: 5th March 2019

In a research-based teaching approach this course covers current hot topics of machine learning for medical informatics. Due to the raising issue of the GDPR, special care is given on explainable-AI and ethical, social, and public issues of Artificial Intelligence for the application in the biomedical domain.
Course homepage: https://hci-kdd.org/machine-learning-for-health-informatics-class-2019


706.315 Mini Course Methods of explainable AI
706.315 Mini Course Methods of explainable AI

Explainability is motivated due to lacking transparency of so-called black-box approaches, which do not foster trust and acceptance of AI. Rising legal and privacy aspects, e.g. with the new European GDPR (which come into effect in May 2018) will make black-box approaches difficult to use in Business (see explainable AI).
Course Homepage: https://hci-kdd.org/methods-of-explainable-ai


185.A83 Machine Learning for Health Informatics (class of 2018) Start: 6.3.2018
185.A83 Machine Learning for Health Informatics (class of 2018) Start: 6.3.2018

We foster a research-based teaching approach on current topics of machine learning for the application in health informatics. Due to the importance, a special focus is this year given on explainable-AI and ethical, social, public issues of Artificial Intelligence for health
Course homepage: https://hci-kdd.org/machine-learning-for-health-informatics-class-2018/


706.046 AK HCI - Intelligent User Interfaces - Towards explainable-AI (class of 2018) Start: Mo, 5.3.2018
706.046 AK HCI - Intelligent User Interfaces - Towards explainable-AI (class of 2018) Start: Mo, 5.3.2018

Explainable-AI (eXAI) is of increasing importance due to legal aspects (GDPR). The goal is to make AI/ML (see definition) approaches transparent, understanable and  re-traceable. Ideally we make use of cognitive capabilities of a human-in-the-loop in a glass-box approach. This needs new concepts of explainable IUI’s [Go to Course-Homepage]


340.300 Principles of Interaction: Interaction with Agents and Federated ML (class of 2017)
340.300 Principles of Interaction: Interaction with Agents and Federated ML (class of 2017)

In this part of LV 340.300 of Linz University students get to know the latest insights into collaborative interactive machine learning approaches and interaction with multi-agents and the human-in-the-loop with a focus on federated machine learning approaches. Go to Course Homepage: http://hci-kdd.org/interactive-machine-learning


Mini Course MAKE-Health: Machine Learning & Knowledge Extraction for Health Informatics (class of 2017)
Mini Course MAKE-Health: Machine Learning & Knowledge Extraction for Health Informatics (class of 2017)

This mini course at the University of Verona follows a research-based teaching (RBT) approach and discusses experimental methods for combining human intelligence with machine learning to learn from prior health data and to extract and discover knowledge from health data. Go to the Course Homepage


185.A83 Machine Learning for Health Informatics (class of 2017)
185.A83 Machine Learning for Health Informatics (class of 2017)

This course considers the whole pipeline from data preprocessing to data visualization and fosters the HCI-KDD approach, which encompasses a synergistic combination of methods from two areas to understand intelligence: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with artificial intelligence Go to the Course Homepage


706.046 AK HCI - Intelligent User Interfaces IUI (class of 2017)
706.046 AK HCI - Intelligent User Interfaces IUI (class of 2017)

Intelligent User Interfaces (IUI) is where HCI meet the possibilities and new aspects of Artificial Intelligence (AI), often defined as the design of intelligent agents – the core essence in Machine Learning. In this course, Software Engineering is seen as dynamic, interactive and cooperative process which facilitate an optimal mix of standardization and tailor-made solutions. Go to Course Hompeage


706.315 Interactive Machine Learning - iML (class of 2016)
706.315 Interactive Machine Learning - iML (class of 2016)

This graduate course follows a research-based teaching (RBT) approach and provides a broad overview of models and discusses methods for combining human intelligence with machine intelligence to extract knowledge from data. The application focus is on the health informatics domain. Go to course homepage


709.049 Biomedical Informatics: discovering knowledge in (big) data (since 2010)
709.049 Biomedical Informatics: discovering knowledge in (big) data (since 2010)

The course covers computer science aspects of biomedical informatics (= medical informatics + bioinformatics).  The focus is on knowledge discovery from data by machine learning and concentrating on algorithmic and methodological issues of data science. Health Informatics is the field where machine learning has the greatest potential to provide benefits in improved medical diagnoses, disease analyses, decision making and drug developments with high real-world economic value.

Go to the course homepage


185.A83 Machine Learning for Health Informatics 2016
185.A83 Machine Learning for Health Informatics 2016

Machine Learning is the most growing field in computer science (Jordan & Mitchell, 2015. Machine learning: Trends, perspectives, and prospects. Science, 349, (6245), 255-260), and it is well accepted that Health Informatics is amongst the greatest challenges (LeCun, Bengio, & Hinton, 2015. Deep learning. Nature, 521, (7553), 436-444).

For the successful application of Machine Learning in Health Informatics a comprehensive understanding of the whole HCI-KDD-pipeline, ranging from the physical data ecosystem to the understanding of the end-user in the problem domain is necessary. In the medical world the inclusion of privacy, data protection, safety and security is mandatory.

Keywords: Automatic machine learning, interactive machine learning, doctor-in-the-loop, subspace clustering, protein folding, k-Anonymization

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706.315 Interactive Machine Learning 2015
706.315 Interactive Machine Learning 2015

Whilst in classic ML usually there is little or no end users’ feedback (a Google car is intended to go without human-in-the-loop), iML takes the human-into-the-loop, hence let the end users’ control the learning behaviour: putting the huge potential of modern sophisticated machine learning algorithms into the hands of domain experts – so that the machine can benefit from the knowledge of this experts.

Keywords: Interactive Learning and Optimization with the Human in the Loop, Hybrid Learning Systems, Active Learning, Active preference learning, reinforcement learning,

Go to the Course Homepage


706.046 AK HCI - Intelligent User Interfaces - HCI meets AI 2016
706.046 AK HCI - Intelligent User Interfaces - HCI meets AI 2016

Intelligent User Interfaces (IUI) is where the Human-computer interaction (HCI) aspects meet Artificial Intelligence (AI), often defined as the design of intelligent agents – the core essence in Machine Learning. In this practically oriented course, Software Engineering is seen as dynamic, interactive and cooperative process which facilitate an optimal mixture of standardization and tailor-made solutions.

Keywords: Experimental Software Engineering, Intelligent User Interfaces, Artificial Intelligence, Machine Learning

Go to Course Hompeage


709.049 Medical Informatics / Medizinische Informatik 2015
709.049 Medical Informatics / Medizinische Informatik 2015

This course covers computer science aspects of biomedical informatics (= medical informatics + bioinformatics) with a focus on discovering knowledge from big data  concentrating on algorithmic and methodological issues. Medicine and Biology are turning more and more into a data science, consequently the focus of this lecture is on interactive knowledge discovery/data mining and interactive machine learning.

Keywords: Biomedical Informatics, Data, Information, Knowledge

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Previous lecture slides from the last semester are available via:

http://genome.tugraz.at/medical_informatics.shtml


706.318 DissertantInnenseminar - Ph.D. Seminar (every year)
706.318 DissertantInnenseminar - Ph.D. Seminar (every year)

For students of the doctoral school Computer Science (Informatik) – the seminar is compulsory, the aim is to help the doctoral candidates to improve their research work and their communicationa and presentation of their scientific field.

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706.315 Selected Topics on Interactive Knowledge Discovery 2014
706.315 Selected Topics on Interactive Knowledge Discovery 2014

Our data-centric world – from genome sequencing to digital surveys of space – generates tremendous amounts of complex high-dimensional data sets. Approaches from algebraic topology may help to understand such data, however, the challenge is in making such approaches interactive, hence to include the human into the loop at the very beginning of the knowledge discovery and data mining process.

https://online.tugraz.at/tug_online/lv.detail?cperson_nr=5313&clvnr=175496


444.152 Medical Informatics / Medizinische Informatik (since 2012 every year)
444.152 Medical Informatics / Medizinische Informatik (since 2012 every year)

2VO, 3 ECTS, WS 13/14, Course starts: Di, 15.10.2013, Graz University of Technology, Faculty of Electrical and Information Engineering, Insitute of Genomics and Bioinformatics; The focus of this lecture is on knowledge discovery and data mining [Link to TUG-Online],

all lecture slides available via: http://genome.tugraz.at/medical_informatics.shtml


706.046 AK HCI Mensch-Maschine Kommunikation: Applying User-Centered Design 2014
706.046 AK HCI Mensch-Maschine Kommunikation: Applying User-Centered Design 2014

3 VU, 5 ECTS, SS 13, Selected chapters of Human–Computer Interaction & Usability Engineering (HCI&UE), Graz University of Technology, Faculty of Informatics, Institute for Information Systems and Computer Media (IICM), [Link to TUG-Online]


706.117 DiplomandInnen Seminar (every year)
706.117 DiplomandInnen Seminar (every year)

3 SE, 5 ECTS, WS 12/13, Diplomandinnen und Diplomanden Seminar, Graz University of Technology, Faculty of Informatics, Institute for Information Systems and Computer Media (IICM), [Link to TUGOnline]


Knowledge Management in Health Care and Hospital Information Systems (since 2011 every year - blocked)
Knowledge Management in Health Care and Hospital Information Systems (since 2011 every year - blocked)

2 VO/SE, Informations- und Wissensmanagement im Gesundheitswesen (Wirtschafts Informatik), Health Care Management, Wirtschaftsuniversität Wien (WU Wien), 2011/12, Winter Term Postgraduate MBA Course [Link to Course]


400.141 Knowledge Acquisition, Information and Visualiszation 2006-2011 (compulsory, each semester)
400.141 Knowledge Acquisition, Information and Visualiszation 2006-2011 (compulsory, each semester)

3 VO, 3 ECTS, SS 11, Knowledge, Information and Visualization, Medical University Graz, Institute of Medical Informatics, Statistics and Documentation (IMI), Medical University Graz (MUG), Graduate Course for Medical Students, and Postgraduate for PhD students of Life Sciences. Course content: Multimedia Hospital Information Systems and Decision Support.