Unsupervised Learning
Bachelor of Science in Computer Science · University · Artificial Intelligence
This unit introduces unsupervised learning algorithms, enabling students to discover hidden patterns and structures in unlabeled data. By mastering techniques like clustering, dimensionality reduction, and anomaly detection, students will gain the ability to extract valuable insights and make informed decisions.
Zum Üben gibt es 52 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Introduction to Unsupervised Learning
- Clustering Algorithms (e.g.
- k-means
- hierarchical clustering)
- Dimensionality Reduction Techniques (e.g.
- PCA
- LDA)
- Anomaly Detection Algorithms (e.g.
- isolation forest
- local outlier factor)
- Applications of Unsupervised Learning in Different Domains
Stichworte
- Clustering
- Dimensionality Reduction
- Anomaly Detection
- Unsupervised Learning
- Machine Learning
- Data Analysis
- Pattern Recognition