Displaying 263 resources



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.



Decision Surfaces. Support Vector Machines
This lecture overviews Decision Surfaces and, in particular, Support Vector Machines that have many applications in Machine Learning and Pattern Recognition. It covers the following topics in detail: Decision surfaces. Hyperplanes.



Agent Systems
This lecture overviews Agent Systems that has many applications in multi-party behavior modeling.



1D Convolutional Neural Networks
This lecture overviews 1D Convolutional Neural Networks that has many applications in 1D signal analysis.



Graph-Based Pattern Recognition
This lecture overviews Graph-Based Pattern Recognition that has many applications in data clustering and dimensionality reduction.



Introduction to Machine Learning
This lecture will cover the basic concepts of Machine Learning to alleviate inconsistencies towards concept and notation accuracy. Supervised, self-supervised, unsupervised, semi-supervised learning. Multi-task Machine Learning.