By the end of this course you should be able to:
- Explain the kinds of problems suitable for Unsupervised Learning approaches.
- Explain the curse of dimensionality, and how it makes clustering difficult with many features.
- Describe and use common clustering and dimensionality-reduction algorithms.
- Try clustering points where appropriate, compare the performance of per-cluster models.
- Understand metrics relevant for characterizing clusters.