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.
Dimensionality Reduction
This lecture overviews Dimensionality Reduction that has many applications in object clusring and object recognition. It covers the following topics in detail: Feature selection. Principal Component Analysis. Linear Discriminant Analysis.
Graph-Based Pattern Recognition
This lecture overviews Graph-Based Pattern Recognition that has many applications in data clustering and dimensionality reduction.
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.