Advanced Certificate in Modern Spectral Clustering

• Fundamentals of Spectral Clustering: Introduction to spectral clustering, distance metrics, similarity measures, and graph Laplacian.
• Advanced Spectral Clustering Algorithms: Normalized cuts, ratio cuts, and other advanced spectral clustering techniques.
• Kernel Methods in Spectral Clustering: Kernel functions, kernel spectral clustering, and their applications.
• Constrained Spectral Clustering: Constrained clustering with spectral techniques and their impact on clustering quality.
• Scalable Spectral Clustering: Techniques for scaling spectral clustering to large datasets, such as sparse representation and randomized algorithms.
• Evaluation Metrics for Clustering: Internal and external evaluation metrics for assessing clustering performance.
• Real-World Applications of Spectral Clustering: Case studies and applications of spectral clustering in computer vision, natural language processing, and bioinformatics.
• Deep Learning and Spectral Clustering: Integration of deep learning techniques and spectral clustering for improved clustering performance.
• Spectral Clustering Challenges and Future Directions: Discussion of current challenges and future directions in spectral clustering research.