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Lecture notes on reinforcement learning
Reinforcement learning is an appealing subject. Firstly, it is a very general concept: an agent interacts with an environment with the goal to maximize
the rewards it receives from the environment.
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.
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.
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.