Course Recordings

Recordings

• Lecture 1: - Introduction and Course topics
• Lecture 2: Random Walks on Graphs and Cummute Time
  
• Lecture 3: Random Walks on Graphs and Mixing
  
• Lecture 4: - Random Walks on Graphs and Mixing
• Lecture 5: - Perron Frobenius and Symmetric Perron Frobenius
• Lecture 6: - Differential Equations, Matrix Exponentials and Laplacians
• Lecture 7: - Bounding Eigenvalues, Courant-Fischer, and Path Embedding
• Lecture 8: - Graph Sparsifiers and Ahlswede-Winter Thm
• Lecture 9: - Matrix Chernoff Bounds, Ahlswede-Winter, Tropp
• Lecture 10: - Golden-Thompson using Weyl’s Majorant Theorem
• Lecture 11: - Graph Cuts and Eigenvalues: Cheeger inequality
• Lecture 12: - Direct Linear Solvers and Nested Dissection for Planar Graphs
• Lecture 13: - Solving Linear Systems: The Basic Iterative Method, Extrapolated Method, Chebyshev acceleration
• Lecture 14: - Conjugate Gradient Method and Steepest Descent
• Lecture 16: - Conjugate Gradient Method Analysis
• Lecture 17: - Fiedler's Thm and Generalized Laplacian's
• Lecture 18: - Preconditioned Conjugate Gradient Method and Low Stretch Spanning Trees
• Lecture 19: - Eigenvalues and Vectors by Iterative Methods
• Lecture 20: - Graph Maximum Cut via Spectral
• Lecture 21: - Graph Maximum Cut via Spectral Continued
• Lecture 22: - No Recording
• Lecture 23: - Solving Symmetric Diagonally Dominate Linear Systems
• Lecture 24: - "Random Walks with Restarts and Spilling Paint
• Lecture 25: - Counting Random Trees
• Lecture 26: - The Markov Chain Tree Theorem
• Lecture 27: - No Recording
• Lecture 28: - Random Walks and Matching
• Lecture 29: - Maximum Flow via Electrical Flow
• Lecture 30: - Nesterov and LRS MaxFlow
• Lecture 31: - Probability 101, The Exponential Dist., Low Diameter Decomposition
• Lecture 32: - Exp. Dist. and Spanners