Subject » BMEVITMMB10
Advanced Data Analysis Methods Laboratory
Haladó adatelemzési módszerek labor
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
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| Subject name (Hungarian, English) |
Haladó adatelemzési módszerek labor
Advanced Data Analysis Methods Laboratory
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| Subject code | BMEVITMMB10 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
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| Assessment type | félévközi érdemjegy | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
DR. Toka László
position: egyetemi tanár
contact:
toka.laszlo@vik.bme.hu
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| Responsible department |
Távközlési és Mesterséges Intelligencia Tanszék
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| 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
1. Selection and interpretation of the data mining task, project planning, and documentation of the evaluation criteria for future solutions. Subsequently, complete data mining cycles and redefine them by evaluating the following work stages:
2. Data Preparation (selection of the database and data format, data cleansing, etc.)
3. Data Visualization and Analysis (correlation analysis, explanatory variable selection, data transformations, etc.)
4. Generation of Machine Learning Models (model selection, hybrid, deep learning, etc.)
5. Evaluation of Machine Learning Models (metric selection, bootstrapping, improving results, hyperparameter tuning, applying boosting, etc.)
6. Practical application of the generated data mining process (deployment to the cloud, ethical considerations, data protection).
The aim of the course is to deepen theoretical knowledge and practical skills acquired in the Data Science and Artificial Intelligence specialization through the execution of a specific data mining project.
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
The independent solution of programming tasks related to the milestones defined in the curriculum will be assigned as homework, with the presentation of solutions during the laboratory sessions.
Tanulástámogató anyagok
Online források
[1] Jiawei Han, Micheline Kamber, Jian Pei: Data Mining Concepts and Techniques (Third Edition), 2012, https://myweb.sabanciuniv.edu/rdehkharghani/files/2016/02/The-Morgan-Kaufmann-Series-in-Data-Management-Systems-Jiawei-Han-Micheline-Kamber-Jian-Pei-Data-Mining.-Concepts-and-Techniques-3rd-Edition-Morgan-Kaufmann-2011.pdf; [2] scikit-learn, 2024, https://scikit-learn.org/; [3] pandas, 2024, https://pandas.pydata.org/; ; [4] TensorFlow, 2024, https://www.tensorflow.org/
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)
Data Science, Artificial Intelligence, Data Analysis, Statistics, Probability Theory
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)
Data Science, Artificial Intelligence, Data Analysis, Statistics, Probability Theory
General rules
Requirements:
The semester work is organized along the milestones specified in this thematic plan every two weeks. The requirement is to successfully complete at least 4 out of 6 milestones within the specified time, meaning that delaying 2 milestones during the semester is permitted. The final grade for the semester is calculated based on the results of the 6 milestones and the end-of-year report grade. The weekly schedule is as follows:
1. Introduction, presentation of available tasks, task assignment (attendance required)
2. Online consultation opportunity during scheduled hours
3. M1: Presentation of project plan, data preparation plan (attendance required)
4. Online consultation opportunity during scheduled hours
5. M2: Presentation of data preparation, data visualization plan (attendance required)
6. Online consultation opportunity during scheduled hours
7. M3: Presentation of data visualization, ML models plan (attendance required)
8. Online consultation opportunity during scheduled hours
9. M4: Presentation of ML models, ML model evaluation plan (attendance required)
10. Online consultation opportunity during scheduled hours
11. M5: Presentation of ML model evaluation, deployment plan (attendance required)
12. Online consultation opportunity during scheduled hours
13. M6: Presentation of application (attendance required)
14. Opportunity for make-up sessions
Additional possibilities:
In case of missing a maximum of 2 milestones, it is mandatory to complete the outstanding results by the deadline of the next milestone (or, in the case of the 6th milestone, by the penultimate week of the semester).
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
Mandatory: Deep Learning
Recommended: Databases, Artificial Intelligence
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Requirements valid from:
—
Requirements valid until:
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Curriculum placement
No curriculum placements recorded for this subject version.