Spectral Clustering (2020 fall). Online course on Fridays 3:15--5 pm. Join via Teams, code: iofepl5 IMPORTANT: I accept more homework solutions until 11 December, 3pm. On 11 December, 3:15pm, you can find a final test on my homepage here for those who have the signature by that time. You have to send me the solutions before 4:45pm in one pdf attachment, handwritten. Based on that and the midterm assignment, I will offer you a grade.

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• Final test on 11 December
• Exam topics
• Course requirements
• Topic description
• Midterm assignments
• Material of Lesson 1 (Quadratic placement and Laplacian matrices)
• Material of Lesson 2 (Normalized Laplacian and modularity matrices, RKHS)
• Material of Lesson 3 (Normalized cuts and the isoperimetric number)
• Material of Lesson 4 (Random block matrices, discrepancy)
• Material of Lesson 5 (Graph convergence, testable graph parameters)
• Material of Lesson 6 (Generalized random and quasirandom graphs)
Material of Lesson 7 (Parameter estimation via the EM algorithm in probabilistic block models)
• Basics: Real Matrices
• Basics: Random Matrices
• We follow the book (some parts will be skipped):
  Spectral Clustering and Biclustering. Learning Large Graphs and Contingency Tables, 2013, Wiley.