Subject » BMEVIMIMB10
Trustworthy AI and Data Analysis
Megbízható mesterséges intelligencia és adatelemzés
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
—
| Subject name (Hungarian, English) |
Megbízható mesterséges intelligencia és adatelemzés
Trustworthy AI and Data Analysis
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVIMIMB10 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
|
||||||||||||
| Assessment type | vizsga | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
DR. Gönczy László
position: egyetemi docens
contact:
gonczy.laszlo@vik.bme.hu
|
||||||||||||
| Responsible department |
Mesterséges Intelligencia és Rendszertervezés Tanszék
|
||||||||||||
| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Programme
Detailed topics of the lectures:
- Fundamental concepts of trustworthiness in data analysis and Artificial Intelligence. Approaches to reliable data analysis and artificial intelligence, human-centered artificial intelligence. Ethical background of analysis and AI, legal regulations, standardization, and integration in engineering best practices.
- Data quality and veracity. Validation of input datasets: goals and applications of exploratory data analysis. Measurement of data quality, data processing, tidy data, ETL / ELT frameworks, automated data processing and visualization. Use of engineering assumptions in data analysis: considering causal, temporal, and topological relationships.
- Understanding and explainability of data through data visualization: comparison, trend analysis, outlier detection, determining relationships, clustering. Use cases of visualization and their supporting technologies: monitoring/dashboard, business reporting, evaluation of alternatives/hypotheses, reproducible research.
- Evaluation, testing, and assurance of data analysis and machine learning models: defining performance metrics, evaluating alternatives, visual support for evaluating results and parameterization. Sensitivity analysis, examination of variable importance.
- Data analysis lifecycle. Cloud-based systems. Application of blockchain in the data sharing process.
- Use of qualitative models to describe the construction and changes of reliable systems. Validation of qualitative models/model details based on measured data.
- Data-driven model building: methods and applications of process mining: model building, conformance checking, log analysis, fraud detection. Parameterization of business rule systems based on data, rule mining.
- Use of intelligent learning methods in critical systems. Application of fault-tolerant patterns. Test generation for AI services.
- Reliable and explainable artificial intelligence: black and white box approaches. Probabilistic and causal models.
- Reliable probabilistic, causal, decision-theoretic, and counterfactual reasoning.
- Interpretable AI models in the formalization of AI: explainability, utility, fairness.
- Lifecycle of white box models, auditing, evaluation, and risk analysis of models: ALTAI approach, process of model acceptance/adoption, analytical/hybrid methods, model testing, explanation generation.
- Explainability of black box models, model derivation.
- Reliable human-machine hybrid systems, "human in the loop" approach, reliable multi-agent systems.
Detailed topics of the exercises:
- Data quality evaluation, transformation and validaiton of input data, data profiling.
- Visual Exploratory Data Analysis, automated visualization derivation.
- Application of process mining in model building and validation.
- Test generation for black box testing of AI models.
- Sensitivity analysis of models, examination of variable importance, CP, PDP, Shapley DALEX.
- Derivation of interpretable models, representation of dependencies and causal relationships.
- Methods of explanation generation, generating logical, probabilistic, and causal explanations.
The results of artificial intelligence, machine
learning, and data analytics are increasingly used for several real-life
purposes as a service embedded in complex IT systems. However, the operational
safety of these IT systems is currently often not addressed, as their correct
functioning is typically not guaranteed, there are no standardized
development/testing methods, the robustness of such systems is not ensured, and
they are not protected against accidental or malicious input errors. However,
there is a wide range of research and regulatory activity to improve
reliability, which has led to new ethical, legal, technological, and
theoretical approaches to managing societal-level risks.
The objective of this course is to introduce
the approaches, concepts, and engineering best practices of trustworthy data
analysis, machine learning, and artificial intelligence. The course will also
review issues related to the integration of intelligent algorithms into IT
systems, methods for data-driven solutions to technical problems, and
integration of these into development/operations processes.
The course will introduce the human-centered
approach to data analytics and artificial intelligence at a societal level, its
ethical background, legal regulation, its representation in standards, and its
implementation in engineering practice. For both data analytics and AI, it will
present the potential and limitations of interpretability, explainability,
testability, and sensitivity analysis. It describes the comprehensive
formalization of the data analysis workflow and the lifecycle of creating an AI
service/product, specifically validated documentation, with the potential of
using blockchain tools and the auditing of the result.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
2 hours of lectures per week, 1 hour of practice
Tanulástámogató anyagok
Online források
Presentations, notes, interactive Jupyter Notebooks.; Russell, Stuart J., Peter Norvig: Artificial intelligence a modern; approach. Pearson Education, Inc., 2010.; Theus, Martin, and Simon Urbanek. Interactive graphics for data; analysis: principles and examples. CRC Press, 2008.; Wickham, Hadley; Grolemund, Garrett (2017). R for Data Science :; Import, Tidy, Transform, Visualize, and Model Data. Sebastopol, CA:; O'Reilly Media. ISBN 978-1491910399. (online); Antal Péter (szerk.). Intelligens adatelemzés. Typotex Kiadó,; 2014. Online.; Biecek, Przemyslaw, and Tomasz Burzykowski. Explanatory model; analysis: Explore, explain and examine predictive models. Chapman and; Hall/CRC, 2021. (online); Cristoph Molnar. Interpretable Machine Learning: A Guide For Making; Black Box Models Explainable, Second Edition, 2022. (online)
Recommended preliminary knowledge for completing the subject
Knowledge type competencies
(azon előzetes ismeretek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
Basic
statistical knowledge, data structures, algorithms, fundamental concepts of
artificial intelligence.
Skill type competencies
(azon előzetes képességek és készségek összessége, amelyek megléte nem kötelező, de a tantárgy eredményes teljesítését nagyban elősegíti)
nincs
Recommended (non-compulsory) preliminary competencies
(azon ajánlott (nem kötelező) előzetesen megszerzendő kompetenciák összessége, amelyek jelentősen hozzájárulnak a tantárgy eredményes teljesítéséhez)
Basic
statistical knowledge, data structures, algorithms, fundamental concepts of
artificial intelligence.
General rules
Requirements:
During the semester:
Homework
assignment, where students are required to complete an individual task on a
topic and dataset agreed upon with the instructors.
During the exam period:
For
courses with less than 30 students, oral exams are conducted; otherwise,
written exams will be held. Exams cover both theoretical concepts and their
practical application.
Additional possibilities:
Homework
can be re-submitted during the repeat period.
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Mathematical
statistics.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
—
Curriculum placement
No curriculum placements recorded for this subject version.