About the content
Discover the basic concepts of cluster analysis, and then study a set of typical clustering methodologies, algorithms, and applications. This includes partitioning methods such as k-means, hierarchical methods such as BIRCH, and density-based methods such as DBSCAN/OPTICS. Moreover, learn methods for clustering validation and evaluation of clustering quality. Finally, see examples of cluster analysis in applications.
- Week 1 - Course Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
- Week 1 - Module 1
- Week 2 - Week 2
- Week 3 - Week 3
- Week 4 - Week 4
- Week 4 - Course Conclusion
In the course conclusion, feel free to share any thoughts you have on this course experience.
Abel Bliss Professor
Department of Computer Science
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