| Detailed information |
| Original study plan |
Bachelor's programme Artificial Intelligence 2019W |
| Objectives |
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.
|
| Subject |
- 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
|
| Criteria for evaluation |
The final grade is based on a combined assessment of homework and a final exam.
|
| Methods |
Slide presentations complemented by online demos and examples presented on the blackboard; demos are based on the scientific computing platform R
|
| Language |
English |
| Study material |
Electronic course material and example code are made available for download
|
| Changing subject? |
No |
| Further information |
Until term 2019S known as: 875BIMLMDAK16 KV Basic Methods of Data Analysis
until term 2016S known as: 675MLDAMDAK13 KV Basic Methods of Data Analysis
|
| Earlier variants |
They also cover the requirements of the curriculum (from - to)
875BIMLMDAK16: KV Basic Methods of Data Analysis (2016W-2019S)
675MLDAMDAK13: KV Basic Methods of Data Analysis (2013W-2016S)
|
