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Related lectures (20)

Page 2 of 2

Graphical Models: Joint Probability Distribution

Covers the concept of graphical models and joint probability distributions.

Graphical Models: Representing Probabilistic Distributions

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

Isomorphic Graphs: Definition and Examples

Explains isomorphic graphs through examples and the relationship with identical graphs.

Belief Propagation

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Graph Surfaces: Integration and Vector Calculus

Covers integrating functions over graph surfaces in vector calculus, emphasizing the interpretation of divergence theorem and special cases of domain between two graphs.

Bethe Free Entropy

Covers the computation of Bethe free entropy and the interpretation of messages between variables and factors.

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Graph Coloring: Basics and Applications

Covers the basics and applications of graph coloring, including balancing vectors and achieving perfect fairness.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Tangent Lines and Approximation

Covers the concept of tangent lines and function approximation near a point.