Detailinformationen |
Quellcurriculum |
Bachelorstudium Artificial Intelligence 2024W |
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.
|
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
|
Beurteilungskriterien |
(*)The final grade is based on a combined assessment of homework and a final exam.
|
Lehrmethoden |
(*)Slide presentations complemented by online demos and examples presented on the blackboard; demos are based on the scientific computing platform R
|
Abhaltungssprache |
Englisch |
Literatur |
(*)Electronic course material and example code are made available for download
|
Lehrinhalte wechselnd? |
Nein |
Frühere Varianten |
Decken ebenfalls die Anforderungen des Curriculums ab (von - bis) 536DASCBMDAK19: KV Basic Methods of Data Analysis (2019W-2024S) 875BIMLMDAK16: KV Basic Methods of Data Analysis (2016W-2019S) 675MLDAMDAK13: KV Basic Methods of Data Analysis (2013W-2016S)
|