185.A83 Machine Learning for Health Informatics (class of 2020) Start: 10th March 2020 (3 ECTS, 2 h, G)
This course follows a research-based teaching approach. Topics include methods for combining human intelligence with machine intelligence for medical decision support. Health is increasingly developing into a data science, consequently robust medical AI solutions are needed to enable ethically responsible machine learning, so that humans and computers can jointly make best possible medical decisions. The new general data protection regulation of the European Union explicitly includes a “right for explanations”. The EU Parliament recently passed a resolution on “explainable AI”. This course focuses on making machine decisions transparent, comprehensible and thus interpretable for a medical expert. A future requirement will be to enable medical experts to understand the context and the underlying explanatory factors why a certain machine decision was made, as well as to ask counterfactual questions, such as “what if” questions in human AI dialogue systems.
Seminar Explainable AI 2019
This course consists of 10 modules: Module 0 is voluntary for students who need a refresher on probability and information; Module 9 is mandatory, on “Ethical, Legal and Social Issues of Explainable AI”, where we deal with bias, fairness and trust of machine learning which is very important for ensuring robustness; Modules 1 to 8 are adaptable to the indiviual needs and requirements of the class and deal with methodological aspects of explainable AI and interpretable ML in a research-based teaching (RBT) style; this seminar organically grown from various courses on interactive machine learning and decision support; we speak Python, and experiment with Kandinsky-Patterns, our “Swiss-Knife” for the study of explainable ai (watch this video to get an idea). [Syllabus xAI 2019, pdf, 85kB]
Course Hompage: https://human-centered.ai/seminar-explainable-ai-2019/
GitHub page: https://github.com/human-centered-ai-lab/cla-Seminar-explainable-AI-2019
ECML-PKDD Tutorial 2019: From interactive Machine Learning to Explainable AI
Explainability, Fairness and Robustness are the magical three components for successful applications of medical Artificial Intelligence (“medical AI”) in the future. In this tutorial we learn a few methodological basics on “explainable AI”, “interpretable machine learning” and “ethical responsible machine learning”. Moreover, we will explore the necessity to answer questions of “what is a good explanation?” with causability measures (see our Systems Causability Scale SCS) and to determine “to whom do we need the explanation”. This basic knowledge is needed for the design, development, testing and evaluating future Human‐AI interfaces, including Q/A systems and dialog systems. Hands-on we work with an open experimental explanation environment  the so‐called KandinskyPatterns, which can be used to experiment in the broader field of xAI.
 Project Homepage: https://human‐centered.ai/project/kandinsky‐patterns
185.A83 Machine Learning for Health Informatics (class of 2019) Start: 12th March 2019 (3 ECTS, 2 h, G)
In this course we cover some current topics of data driven AI/machine learning for medicine and health. The focus is on explainability/transparency, fairness/bias and robustness/trust. We follow a human-centered AI approach and integrate ethical, legal, psychological and sociological issues for the design of interpretable verifiable algorithms for decision support. The goal is to enable human domain experts (e.g. medical doctors) to retrace the results on demand and to be able to understand the underlying explanatory factors (causality) of why an AI-decision has been made, paving the way for ethical responsible AI and transparent verifiable machine learning for decision support. [Syllabus, pdf, 77 kB]
706.315 Selected Topics on interactive Knowledge Discovery: From explainable AI to causability (class of 2019, for PhDs only, 2 h PG)
706.046 AK HCI - Intelligent User Interfaces - Towards explainable-AI (class 2019) Start: 11th March 2019 (5 ECTS, 5 h, G)
Explainable-AI (xAI) is actually an old field, nowadays gaining interest for science, industry and society. xAI is relevant for various application domains, particularly for medicine and health. Prestigious international academic institutions e.g. Carnegie Mellon, MIT, but also commercial institutions such as Google see here, are emphasizing the importance of a more human-centered AI and the necessity to design effective and efficient Human-AI interfaces, e.g. conversational interfaces. The goal is to make AI/ML (see definition) results, the “machine decisions” re-traceable, thus interpretable and understandable for a domain expert – on demand. One aspect includes contextual adaptive dialogue systems (so-called “Explanation-Interfaces”), where human understandable natural language (NLP) plays a central role for future question-answer (Q/A) dialogs.
Course Homepage: https://human-centered.ai/intelligent-user-interfaces-2019/
706.998/706.999 DiplomandInnen/DissertantInnen Seminar (class of 2019, 3 ECTS, 2h UG, G)
This course is for master and particularly doctoral students of the Holzinger group; the seminar is compulsory, the aim is to help the master/doctoral candidates to improve their research work strategies along with their communication and presentation abilities within their scientific field. A particular focus is given on ethical, social, legal and privacy aspects of human-centered ai, discussing not only current trends and successes of machine learning but also potential threats, dangers and adversarial effects. The students primarliy learn the tools of the trade, the “Handwerkszeug”, of scientific research. At the same time the students will be made sensible for issues of fairness and trust of modern AI/machine learning in medical AI.
Course Homepage: https://human-centered.ai/scientific-working-for-students
Mini-Course: From Data Science to interpretable AI (class of 2019)
This Mini course is an introduction into (human) decision making, and on how human intelligence can be supported by artificial intelligence (AI) in order to make better decisions. The workhorse for “medical AI” is data driven Machine Learning (ML). After an introcuction into the fundamentals of data, information and knowledge representation, the central focus of this course is on decision making and decision support systems. A special focus is given on what is now called “explainable AI”, where an introducton in causality and interpretability is given, and awareness is raised for ethical-responsible machine learning. This is desirable for many fields of application, but absolutely necessary for medical AI, to foster transparency, fairness, trust and understanding and reducing bias in machine learning. [Syllabus, pdf, 74 kB]
Course homepage: https://human-centered.ai/mini-course-interpretable-ai-class-of-2019/
706.315 Selected Topics on interactive Knowledge Discovery: Methods of explainable AI (for PhDs only, 2 h PG)
The need for Explainability and Interpretability is motivated by lacking transparency of so-called “black-box” machine learning approaches (e.g. deep learning). This is wishable in many domains, but mandatory in the medical domain, where we need to foster trust/acceptance of future AI. Rising legal and privacy aspects (European GDPR, May, 28, 2018) will make “black-box” approaches difficult to use in future Business (see explainable AI), due to the (juridical) “right for explanation”. Other topics of explainable AI include the correlation fallacy and all sorts of biases, e.g. bias in learning but also in interpretation. Our goal is to ensure fairness of machine learning algorithms.
Course Homepage: https://human-centered.ai/methods-of-explainable-ai
185.A83 Machine Learning for Health Informatics (class of 2018) Start: 6th March 2018 (3 ECTS, 2 h, G)
In this course we foster a research-based teaching (RBT) approach on current topics of AI/machine learning for the application in medical decision support. Due to the upcoming importance, a special focus is this years course is given on explainable-AI (and we will learn the differences between explanation and interpretation) and ethical, social, public and legal issues of medical AI for solving real-world problems in health informatics aiming at better medical decision support. We will see that what is called “explainable AI” is an old field, maybe the oldest field in computer science, because the question of “why” is central in the field of causality and is very important for modern data driven medicine.
Course homepage: https://human-centered.ai/machine-learning-for-health-informatics-class-2018/
Mini-Course MAKE-Decisions: From Data Science to Explainable-AI (class of 2018, 3 ECTS, 2h, PG)
This course is an introduction into a core area of health informatics and helps to understand decision making generally and how human intelligence can be supported by Artificial Intelligence (AI) and Machine Learning (ML) (-> What is the difference between AI/ML?) with decision support systems, having a focus on ethical, social and legal aspects. A very old field in AI is what is now called “explainable AI” and is about a) using interpretable methods, or b) making “black-box” approaches interpretable for a human expert.
Course homepage: https://human-centered.ai/mini-course-make-decisions-practice/
706.046 AK HCI - Intelligent User Interfaces - Towards explainable-AI (class 2018) Start: 5th March 2018 (4,5 ECTS, 5 h, G)
Explainable-AI (xAI) is of increasing importance due to legal aspects (GDPR), but also for ethical and social aspects. The goal is to make AI/ML (see definition) approaches re-traceable, transparent, understanable and thus explainable to a human. 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 particularly for ethical responsible data driven machine learning.
Course Homepage: https://hci-kdd.org/iui-explainable-ai
340.300 Principles of Interaction: Interaction with Agents and Federated ML (class of 2017, 3 ECTS, 2 h, G)
In this course Linz University computer science and software engineering students get to know the latest insights into collaborative interactive machine learning approaches, interaction with multi-agents and the human-in-the-loop. A special focus – in the context of what is called “explainable AI” is given on federated machine learning approaches. The application domain is the medical domain, particularly the field of digital pathology (e.g. histophatological images).
Course Homepage: https://human-centered.ai/interactive-machine-learning/
MAKE-Health: Machine Learning & Knowledge Extraction for Health Informatics (class of 2017, 6 ECTS, 3 h, G)
This course at the University of Verona follows a research-based teaching (RBT) approach and discusses theroretical issues and experimental methods for combining human intelligence with artificial intelligence with a strong focus on decision support systems as well as interpretability and explainability (which is now summarized under the term explainable ai and often abbreviated as xai. This course also considers ethical, social and legal aspects of human-centered ai, e.g. evaluating fairness and inclusion and the students should be sensibiliszed on how to ensure trust in future AI systems.
185.A83 Machine Learning for Health Informatics (class of 2017, 3 ECTS, 2h, G)
706.046 AK HCI - Intelligent User Interfaces IUI (class of 2017, 4,5 ECTS, 5 h, G)
Intelligent User Interfaces (IUI) is where HCI meet the possibilities 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. For experimental issues gamification approaches are very useful. Putting the human-in-the-loop is the main focus of this course.
Course Homepage: https://human-centered.ai/iui-where-hci-meets-ai-challenge-2017/
706.315 Interactive Machine Learning - iML (class of 2016, 2 h PG)
This graduate course follows a research-based teaching (RBT) approach and provides an overview of models of human-centered ai and discusses methods for combining human intelligence with machine intelligence (human-in-the-loop). The application focus is on the health informatics domain, where a human must always remain in control (for legal and social issues) – but the principles are useful in any business domain.
Course homepage for 2015 and 2016: https://human-centered.ai/lv-706-315-interactive-machine-learning/
709.049 Biomedical Informatics: discovering knowledge in (big) data (2010-2017, 3 ECTS, 2, UG)
This course covers computer science aspects of biomedical informatics (= medical informatics + bioinformatics). The focus is on machine learning for knowledge discovery from data, and concentrates 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, ultimately contributing to cancer research for the human good. Ethical and social issues are very important aspects!
Course homepage (2010 – 2017): https://human-centered.ai/biomedical-informatics-big-data/
185.A83 Machine Learning for Health Informatics (class of 2016, 3 ECTS, 2 h, G)
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 Go to the Course Homepage
706.315 Interactive Machine Learning (class of 2015, 2 h PG)
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 (class of 2016, 4,5 ECTS, 5 h, G)
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 (2010-2015)
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 Go to the course homepage 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 1 h, PG)
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. Go to Course Homepage
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
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
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, 5 ECTS, 3 h, G)
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]
Probabilistic Decision Making and Statistical Learning Mini-Course (since 2010) blocked Mini-Course), (3 ECTS, 1h, PG)
This Mini-Course is an introduction into a core area of health informatics and helps to understand decision making generally and how human intelligence can be augmented by Artificial Intelligence (AI) and Machine Learning (ML) specifically.
Course Homepage: https://human-centered.ai/mini-course-make-decisions-practice-2019
400.141 Knowledge, Information and Visualiszation 2006-2011 (compulsory, each semester, 3 ECTS, 3 h, UG, G)
This course is compulsory for graduate medical students, and postgraduate PhD students of life sciences. The course covers principles of information systems, fundamentals of decision making and focuses on decision support systems (with hands-on exercises).