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Data Science - Part 2

Adatbányászat - 2
A tantárgyleírás hatályossága
Hatályosság kezdete:
2026. March 21.
Hatályosság vége:
—
Subject name (Hungarian, English)
Adatbányászat - 2
Data Science - Part 2
Subject code BMEVISZA084
Subject type —
Training Level —
Course types and hours (weekly/semester)
Course type lecture tutorial laboratory
hours (weekly) 0 0 2
type (linked/independent) — — autonomous course
Assessment type félévközi érdemjegy
Credits 2
Subject coordinator
DR. Katona Gyula
position: egyetemi tanár
Responsible department
Számítástudományi és Információelméleti Tanszék
Faculty Villamosmérnöki és Informatikai Kar
Subject website www.cs.bme.hu/....
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.  Advanced classification methods: Bagging, boosting, AdaBoost.
2.  Random forest. Implementation of models by WEKA.
3.  Support Vector Machine. Kernel methods, graph kernels. Protein function prediction.
4.  Similarity measures, fingerprint based similarity search. Sketches.
5.  Dimensionality reduction by spectral methods, singular value decomposition, low-rank                             approximation.
6.  Spectral clustering, bi-clustering for microarrays.
7.  Mixture models. Maximum likelihood estimators, EM-algorithm.
8.  Gauss Mixture Models. Midterm test.
9.  Search engines, web information retrieval, PageRank and beyond.
10. Rank learning for Protein Structure Prediction.
11. Text mining, natural language processing. Building databases and networks from PubMed and BioMed Central.
12. Graph mining algorithms. Frequent subgraph mining in microarray-based co-expression networks.
13. Semi-supervised classification of network data, graph stacking in biological networks.
14. Feature selection methods for unbalanced data sets. Final test.

 

The aim of the course is to discuss advanced techniques of data mining with useful knowledge of related disciplines supporting real-world, especially bioinformatics data mining projects. By the end of the course, students will be able to analyze biological (genomic, microarray, pathway, protein, chemical) data sets using complex data mining methods.

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

Handouts, PowerPoint presentations, relevant research papers, web page, course mailing list and Wiki. Weekly regular office hour for consultations.

Tanulástámogató anyagok

Online források
 

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)
The course requires basic knowledge in data mining. (See also the course Data Mining: Models and Algorithms) Background in probability theory and linear algebra is important. Knowledge in combinatorics and algorithms is an advantage. May be studied in the same semester as „Data mining - Part 1”
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)
The course requires basic knowledge in data mining. (See also the course Data Mining: Models and Algorithms) Background in probability theory and linear algebra is important. Knowledge in combinatorics and algorithms is an advantage. May be studied in the same semester as „Data mining - Part 1”
General rules
Requirements: Case study: A practical problem. Choosing the model, solution method. Implementation of the algorithm.         Grading principles:                     Model:                               40%  Solution method:                40% Implementation:                  20%  
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

Not provided.

Workload to complete the subject

No workload breakdown provided.

Validity of subject requirements
Requirements valid from:
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Requirements valid until:
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Curriculum placement

No curriculum placements recorded for this subject version.