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Related lectures (30)
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Statistical Analysis of Network Data
Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.
Bayesian Networks: Factorization and Sampling
Explains Bayesian Networks factorization and sampling methods using DAGs and Variable Elimination.
Model Selection and Local Geometry
Explores model selection challenges in causal models and the impact of local geometry on statistical inference.
Directed Graphs: Introduction and Categories
Covers the fundamental notion of composition in directed graphs and provides examples of their representation.
Network Flows Meets Simplex
Explores network flows, simplex method, linear programming, tree solutions, and dual solutions in optimization problems.
Category Theory: Introduction
Covers the basics of categories and functors, exploring properties, composition, and uniqueness in category theory.
What is a Formal Proof?
Covers the concept of formal proof systems, their structure, and soundness.
Networks: Paths and Components
Explores simple paths, connectivity, equivalence classes, and connected components in directed graphs.
Probability Theory: Markov's Theorem
Explores Markov's theorem, Chernoff bound, and probability theory fundamentals, including good coloring, 2-colorable graphs, and rare events.
Flow Networks: Strongly Connected Components
Introduces Strongly Connected Components and Flow Networks, discussing algorithms and applications.
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