Praktisk

Tirsdag: 13-17:

Forelæsning: 13-15: Bygning 116 - Auditorium 81

Grupperegning: 15-17: Bygning 116 - Lokale 016

Ressourcer

Annoucements

Course Content

ML Reading

Noter:

1: Introduction to Machine Learning

2: Data, feature extraction and PCA

3: Meassures of simillarity, summary statistics and probabilities

4: Probability densities and data visualization

5: Decision Trees & Linear Regression

6: Overfitting and cross-validation

7: Performance Evaluation

8: ANNs & Bias/Variance

9: AUC and ensemble methods

10: K-means and hiarchial clustering