| Detailinformationen |
| Quellcurriculum |
Bachelorstudium Artificial Intelligence 2019W |
| Ziele |
(*)Data analysis and visualization are essential to most fields in science and engineering. The goal of this course is to provide students with a basic tool chest of methods for pre-processing, analyzing, and visualizing scientific data.
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| Lehrinhalte |
(*)- Scatter plots and box plots
- Basics of classification
- Basics of regression
- ANOVA
- Clustering
- Principal component analysis (+ visualization, including spectral maps)
- Singular value decomposition (+ visualization)
- Matrix factorization (+ visualization)
- Independent component analysis
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| Beurteilungskriterien |
(*)The final grade is based on a combined assessment of homework and a final exam.
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| Lehrmethoden |
(*)Slide presentations complemented by online demos and examples presented on the blackboard; demos are based on the scientific computing platform R
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| Abhaltungssprache |
Englisch |
| Literatur |
(*)Electronic course material and example code are made available for download
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| Lehrinhalte wechselnd? |
Nein |
| Sonstige Informationen |
(*)Until term 2019S known as: 875BIMLMDAK16 KV Basic Methods of Data Analysis
until term 2016S known as: 675MLDAMDAK13 KV Basic Methods of Data Analysis
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| Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
875BIMLMDAK16: KV Basic Methods of Data Analysis (2016W-2019S)
675MLDAMDAK13: KV Basic Methods of Data Analysis (2013W-2016S)
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