This is the main course website for the seminar "Intelligent Data Processes Processes". This is a living document and new content will be added here. The seminar is the final part of a series that started with "Software Technologies and Applied Programming" and was followed by an Introduction to Artficial Intelligence.
Course Description¶
The application areas of artificial intelligence, machine learning and the application-specific aspects of AI processes and projects are dealt with in depth. The efficient application of the learned methods for the use of AI in terms of an AI-based hybrid value creation are designed.
Content¶
Legend¶
| Session was a lecture | |
| Session was a practical | |
| Find slides here | |
| Find external material here |
Project Study Paper¶
Scope & Content¶
Your writing a PSP "AI and Data Strategy" (Digital Loop 1). It is a self-contained work consisting of a 20-page paper and a pitch (2-5 minutes).
In the pitch you will be asked to describe a disruptive business case. The pitch will be graded based on originality, do-ability, AI-content and the quality of the presentation. The written part consists of the formulation of an AI-strategy and the formulation of a data strategy. In terms of do-ability, you should emphasize on a thorough and critical reflection of the quality measurements, the data used to measure success, and the strategies to gather such data.
PSPs are the central examination components of the Experience Based Curriculum. In the PSP, you demonstrate that you are able to approach a real-life (e.g. entrepreneurial) problem by using a scientific approach. The PSP is thus ultimately a measure of your ability to work in a transfer-oriented scientific manner.
Scientific Literature¶
A crucial aspect of demonstrating transfer of scientific knowledge is a proper literature review of current state of the art of your context. The literature needs to be scientific literature. It is accetable to use blogposts or newspaper articles as motivation to your work, but the core of your question needs to be backed by properly published and publicly accessible research. To be clear, this does not mean open access accessibility for every paper in your reference section. But your reference section must not be based on a selection of websites or company-internal material alone that no one else can access.
Summarizing, integral features of a very good PSP are
- a literature section
- ...that cites scientific literature
- ...that reflects the state of the art in that context
- ...that fits the substance of the text
- an interdisciplinary coverage of the topic (potentially)
Scope of your work¶
You are most welcome to illustrate or motivate or evaluate your proposed topic/technique/method by a self-made data analysis. Another way of illustrating your idea is to model the scenario with tools like ER modeling, User Stories, or similar techniques for conceptual models.
Analyzing data or programming anything yourself is, of course, not mandatory. You can also do a proper review of scientific literature deriving recommendations for improvement.
In any case, your work needs to
- contain an appropriate selection of illustrations/analyses/discussions
- be backed by scientific literature
- use a neutral language -- you're not writing marketing material
- be critical regarding the chosen services/techniques/programs
- be critical regarding the chosen metric -- they need to really measure your objectives
- reflect your own work critically discussing if your research/evaluation allows for a substantial conclusion
Important aspects¶
When developing your idea, you might take one or more of the following aspects into consideration:
- What are you aiming to improve?
- What are functional and non-functional requirements of it?
- What is the intended outcome?
- Who is affected by your change?
- How is quality and success going to be measured? What are the KPIs?
- What are possible challenges and pitfalls?
- What is your data describing? What could be possible artifacts?
- Is your data really suitable to measure your KPIs? What does your data express?
Note that not all of these aspects need to be covered in all possible topics. Depending on your focus and question, you may only need to cover some or none of these aspects.
Pitches¶
As part of the PSPs, we will have short elevator pitches of your ideas. You can prepare slides, but they aren't obligatory. The time limit is strict, though. You will have at most 5 minutes - rather prepare for 3 minutes.
Pitch Schedule¶
The pitches are organized as follows:
Each participant is assigned to a group A,B,C,D, or E. In each slot, we will have one person giving a talk. For the other participants to take an active part as well, we will hear one question from a listener and one statement from another listener. The participants asking questions and providing a reviewing statement will be chosen randomly from the respective groups.
To know when your talk is scheduled, please refer to the schedule on moodle.
| Begin | Speaker | Group Asking Question | Group Providing Statement | Group Taking a Break |
|---|---|---|---|---|
| Wednesday, 09:15 | A | B | C | E |
| Wednesday, 09:30 | A | B | C | E |
| Wednesday, 09:45 | B | C | D | A |
| Wednesday, 10:00 | B | C | D | A |
| Wednesday, 10:15 | C | D | E | B |
| Wednesday, 10:30 | C | D | E | B |
| Wednesday, 10:45 | D | E | A | C |
| Wednesday, 11:00 | D | E | A | C |
| Wednesday, 11:15 | E | A | B | D |
| Wednesday, 11:30 | E | A | B | D |
| Wednesday, 11:45 | A | B | C | E |
| Wednesday, 12:00 | A | B | C | E |
| Wednesday, 12:15 | B | C | D | A |
| Wednesday, 12:30 | B | C | D | A |
| Thursday, 09:15 | A | B | C | E |
| Thursday, 09:30 | A | B | C | E |
| Thursday, 09:45 | B | C | D | A |
| Thursday, 10:00 | B | C | B | A |
| Thursday, 10:15 | C | D | E | B |
| Thursday, 10:30 | C | D | E | B |
| Thursday, 10:45 | D | E | A | C |
| Thursday, 11:00 | D | E | A | C |
| Thursday, 11:15 | E | A | B | D |
| Thursday, 11:30 | E | A | B | D |
| Thursday, 11:45 | C | D | E | B |
| Thursday, 12:00 | C | A | A | B |
| Thursday, 12:15 | D | E | A | C |
| Thursday, 12:30 | E | A | B | D |
References¶
- Plamen P. Angelov, Eduardo A. Soares, Richard Jiang, Nicholas I. Arnold, and Peter M. Atkinson. Explainable artificial intelligence: an analytical review. WIREs Data Mining and Knowledge Discovery, 11(5):e1424, 2021. doi:https://doi.org/10.1002/widm.1424.
- Fernando Martínez-Plumed, Lidia Contreras-Ochando, Cèsar Ferri, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana, and Peter Flach. Crisp-dm twenty years later: from data mining processes to data science trajectories. IEEE Transactions on Knowledge and Data Engineering, 33(8):3048–3061, 2021. doi:10.1109/TKDE.2019.2962680.
- Duri Long and Brian Magerko. What is ai literacy? competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI '20, 1–16. New York, NY, USA, 2020. Association for Computing Machinery. doi:10.1145/3313831.3376727.
- Mark Ziemann, Yotam Eren, and Assam El-Osta. Gene name errors are widespread in the scientific literature. Genome Biology, August 2016. http://dx.doi.org/10.1186/s13059-016-1044-7, doi:10.1186/s13059-016-1044-7.
- Jo Bates, Yu-Wei Lin, and Paula Goodale. Data journeys: capturing the socio-material constitution of data objects and flows. Big Data & Society, 3(2):2053951716654502, 2016. doi:10.1177/2053951716654502.
- Barry R Zeeberg, Joseph Riss, David W Kane, Kimberly J Bussey, Edward Uchio, W Marston Linehan, J Carl Barrett, and John N Weinstein. Mistaken identifiers: gene name errors can be introduced inadvertently when using excel in bioinformatics. BMC Bioinformatics, June 2004. http://dx.doi.org/10.1186/1471-2105-5-80, doi:10.1186/1471-2105-5-80.
- Rüdiger Wirth and Jochen Hipp. Crisp-dm: towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining, volume 1, 29–39. Manchester, 2000.
- Pete Chapman, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin Shearer, and Rüdiger Wirth. The crisp-dm user guide. In 4th CRISP-DM SIG Workshop in Brussels in March, volume 1999. sn, 1999.