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

P4.5-Variants of the Gradient Descent Scheme

01 Feb 2024

P4.5-Variants of the Gradient Descent Scheme

P4.6-Batch Normalization

01 Feb 2024

P4.6-Batch Normalization

P4.7-Loss Function Selection and the Softmax Activation Function

01 Feb 2024

P4.7-Loss Function Selection and the Softmax Activation Function

P4.8-ReLU Activation Function

01 Feb 2024

P4.8-ReLU Activation Function

P4.9-Regularization and Dropout

01 Feb 2024

P4.9-Regularization and Dropout

P5.1-Bayesian Learning Introductory Remarks

01 Feb 2024

P5.1-Bayesian Learning Introductory Remarks

P5.10-A Lower Bound Interpretation of EM

01 Feb 2024

P5.10-A Lower Bound Interpretation of EM

P5.11-Exponential Family of Distributions

01 Feb 2024

P5.11-Exponential Family of Distributions

P5.12-The Variational Approximation Method

01 Feb 2024

P5.12-The Variational Approximation Method

P5.13-Variational Inference and Linear Regression

01 Feb 2024

P5.13-Variational Inference and Linear Regression

P5.2-Maximum Likelihood and MAP

01 Feb 2024

P5.2-Maximum Likelihood and MAP

P5.3-Bayesian Generalized Linear Regression

01 Feb 2024

P5.3-Bayesian Generalized Linear Regression

P5.4-Bayesian Regression-Simulation Examples

01 Feb 2024

P5.4-Bayesian Regression-Simulation Examples

P5.5-The Evidence Function

01 Feb 2024

P5.5-The Evidence Function

P5.6-The EM Algorithm

01 Feb 2024

P5.6-The EM Algorithm

P5.7-The EM and the Linear Regression Task

01 Feb 2024

P5.7-The EM and the Linear Regression Task

P5.8-Gaussian Mixture Models

01 Feb 2024

P5.8-Gaussian Mixture Models

P5.9-Clustering, GMMs and the k-Means Algorithm

01 Feb 2024

P5.9-Clustering, GMMs and the k-Means Algorithm

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