A Machine Learning Lecture Series Course Bundle, by Sergios Theodoridis

Sergios Theodoridis

SPS

Members: Free
IEEE Members: Free
Non-members: Free

01 Feb 2024

72 Products in this Bundle

P2.13-AdaBoost

01 Feb 2024

P2.13-AdaBoost

P2.2-Average Risk

01 Feb 2024

P2.2-Average Risk

P2.3-The Gaussian Case

01 Feb 2024

P2.3-The Gaussian Case

P2.4-Minimum Distance and Naive Bayes Classifiers

01 Feb 2024

P2.4-Minimum Distance and Naive Bayes Classifiers

P2.5-k-Nearest Neighbor Rule

01 Feb 2024

P2.5-k-Nearest Neighbor Rule

P2.6-Logistic Regression

01 Feb 2024

P2.6-Logistic Regression

P2.7-Scatter Matrices

01 Feb 2024

P2.7-Scatter Matrices

P2.8-Fisher's Linear Discriminant

01 Feb 2024

P2.8-Fisher's Linear Discriminant

P2.9-Proof of Fisher's Method

01 Feb 2024

P2.9-Proof of Fisher's Method

P3.1-Nonlinear Models and Cover's Theorem

01 Feb 2024

P3.1-Nonlinear Models and Cover's Theorem

P3.10-SVM- The Hinge Loss Function

01 Feb 2024

P3.10-SVM- The Hinge Loss Function

P3.2-Reproducing Kernel Hilbert Spaces

01 Feb 2024

P3.2-Reproducing Kernel Hilbert Spaces

P3.3-Kernel Trick

01 Feb 2024

P3.3-Kernel Trick

P3.4-Examples of Kernels

01 Feb 2024

P3.4-Examples of Kernels

P3.5-Representer Theorem

01 Feb 2024

P3.5-Representer Theorem

P3.6-Kernel Ridge Regression

01 Feb 2024

P3.6-Kernel Ridge Regression

P3.7-Support Vector Regression

01 Feb 2024

P3.7-Support Vector Regression

P3.8-SVM-The Linearly Separable Classes Case

01 Feb 2024

P3.8-SVM-The Linearly Separable Classes Case

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