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Tutorial paper on Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic that has been lately consolidated as a theme of major interest in the deep learning community.
1D Convolutional Neural Networks
This lecture overviews 1D Convolutional Neural Networks that has many applications in 1D signal analysis.
Bayesian Learning
This lecture overviews Bayesian Learning that has many applications in pattern recognition and clustering. It covers the following topics in detail: Bayes probability theorem. Bayes decision rule. Bayesian classification.
Parameter Estimation
This lecture overviews Parameter estimation that has many applications in Statistics and Pattern Recognition.
Hypothesis Testing
This lecture overviews Hypothesis Testing that has many applications in statistics and pattern recognition.
Kernel methods
This lecture overviews Kernel Methods that have many applications in classification and clustering. It covers the following topics in detail: Kernel Trick. Kernel Matrix. Kernel PCA. Kernel correlation and its use in object tracking. Kernel k-means.