Welcome to the Human-Centered AI Research Seminar!

AI/ML (see differences here) is a rapidly growing field in computer science and health informatics is among the greatest application challenges. The Human-centered AI lab (Holzinger group) is dedicated to educate the next generation of data science students able to tackle future challenges of human-centered ai and ethcial responsible machine learning.

This page the general information source for MSc and PhD students and will be adapted e.g. to LV 706.996 (5 ECTS, winter term) and LV 706.998 (5 ECTS, summer term) as well as LV 706.997 (2 h, winter term) and LV 706.999 (2h, summer term). The course will scheduled to the individual needs of each course.

“There are two possible outcomes: if your result confirms your set hypothesis, then you have made a measurement. If the result is contrary to your hypothesis … then you have made a discovery” (attributed to Enrico Fermi (1901-1954))

Module 01 – Part 1: Introduction to Human-Centered AI (Holzinger Group)

In the first part the students get a rough overview on the concepts of human-centered AI and some currently hot topics from Artificial Intelligence and Machine Learning to get a good common understanding and basis for further research in our areas.

Topic 01: The HCAI approach: Towards integrative AI/ML
Topic 02: The complexity of the application area Health Informatics
Topic 03: Probabilistic Information and Probabilistic Learning
Topic 04: Gaussian Processes
Topic 05: Automatic Machine Learning (aML)
Topic 06: Interactive Machine Learning (iML)
Topic 07: Causality, Explainability, Interpretability
Topic 08: The KandinskyPatterns Exploration Environment

Lecture slides 2×2 (pdf, 9,996 kB): 1-Student-Research-Seminar-HOLZINGER-2019-20-PRINT-2×2
Lecture slides full size (pdf, 6,121 kB): 1-Student-Research-Seminar-HOLZINGER-2019-20-PRINT

Note: The slides provided here are for printing and reading, the slides shown will be different from a didactial point of view.

Module 02 – Part 2: Theory of Science and Research Know-how

In the second part the students get a rough overview on the basic concepts of scientific working (Wissenschaftstheorie). We start with answering the question of what science is and why we should contribute to the international scientific community. We are engineers, so do we need the scientific methodological approach? We see science as the field to provide explanations and to make predictions, so it is highly relevant for computer scientists!

Note: Particularly the field of AI/machine learning needs a concerted effort of many people. This also ensures quality control and most of all ethical control.

Topic 01: Motivation: Why should we contribute to the international scientific community?
Topic 02: Overview of (some) basics of the Theory of Science (Wissenschaftstheorie for AI/machine learning researchers)
Topic 03: Fundamentals (Occams Razor, Reichenbach Principle, …)
Topic 05: Methodological approaches

Lecture slides 2×2 (pdf, 9,996 kB):
Lecture slides full size (pdf, 6,121 kB):

Note: The slides provided here are for printing and reading, the slides shown will be different from a didactial point of view.

Module 03 – Part 3: Practice of Science

In the third part the students get a concrete hint on how to write a paper.  We start with how to find, select, read and use a paper. How to start writing and what methods and tools are helpful.

Topic 01: Mechanics: How to contribute to the international scientific community?
Topic 02: First step: How to find, read and use a scientific paper?
Topic 03: Overview on selected conferences and journals relevant for our field
Topic 04: Some practical hints, methods, tools, ethics commisision & co
Topic 05: Last step: Writing a scientific paper

Lecture slides 2×2 (pdf, 9,996 kB):
Lecture slides full size (pdf, 6,121 kB):

Note: The slides provided here are for printing and reading, the slides shown will be different from a didactial point of view.

Module 04 – Part 4: Local Specifics

In the last part the students get some practical hints on the local processes at TU Graz of how to do a Bachelor Thesis, a Master’s Theses and a PhD thesis.

Fast Track:
1) Watch the Students Welcome Video https://goo.gl/GoeBek
2) Read the introduction paper http://www.mdpi.com/2504-4990/1/1/1
3) Read the research and teaching statements, availabe at http://www.aholzinger.at/
4a) PhD-students develop carefully your PhD-proposal PhD-Proposal-HOLZINGER-Group-2017
4b) Latex-Template Masterthesis for Students of the Holzinger Group
4c) Bachelor Students just enroll to course 706.170 at TUGOnline and select Group Holzinger
5) Deans Pages of the Computer Science faculty: https://www.tugraz.at/fakultaeten/infbio/faculty/team/deans-office/

Topic 01: Bachelor – your first academic and/or practical computer science work
Topic 02: Master – your specialization in computer science
Topic 03: PhD – your rocket to science and industry
Topic 04: Specific PhD checklists (from the first discussion to the doctoral defense (Rigorosum))
Topic 05: Everything starts with the PhD proposal: (a) Written document, (b) Presentation, (c) Discussion
Topic 06: Risk mitigation and fallback solutions

Lecture slides 2×2 (pdf, 3,292kB):  4-Student-Research-Seminar-HOLZINGER-2019-20-PRINT-2×2
Lecture slides full size (pdf, 1,874 kB): 4-Student-Research-Seminar-HOLZINGER-2019-20-PRINT

Note: The slides provided here are for printing and reading, the slides shown will be different from a didactial point of view.