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Related lectures (31)

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Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Matrix Solutions: Infinite Possibilities

Explores the infinite solutions of a specific matrix equation for any choice of a constant.

Spatial Response and Convolution

Covers the spatial response of systems, including the calculation of the center of mass and convolution.

Graphs and Probabilities

Explores the connection between graphs and probabilities, emphasizing modular and super modular probabilities and correlation properties.

Matrix Chain Multiplication

Explores dynamic programming for matrix chain multiplication and introduces the concept of the longest common subsequence.

Link Prediction: Missing Edges and Probabilistic Methods

Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.

Matrix Jacobian Function

Explores the concept of Jacobian matrices for differentiable functions and demonstrates their application through a detailed analysis of multiple choice answers.

Consensus Theorem for Communication Networks

Explores the consensus theorem for communication networks and the implications of various consensus properties in networked control systems.

Graphs in Deep Learning: Applications and Techniques

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Networked Control Systems: Properties and Connectivity

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.