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Statistical Learning and Data Science @UCr4ZcPk0siqHBISY-OzFotQ@youtube.com

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I2ML - Supervised Classification - Linear Classifiers

I2ML - Supervised Classification - Naive Bayes

I2ML - Supervised Classification - Discriminant Analysis

I2ML - Supervised Classification - Basic Definitions

I2ML - Supervised Classification - Logistic Regression

I2ML - Supervised Classification - Tasks

I2ML - Random Forest - Bagging Ensembles

I2ML - Random Forests - Out-of-Bag Error Estimate

I2ML - Random Forest - Proximities

I2ML - Random Forest - Feature Importance

I2ML - Random Forest - Basics

I2ML - Tuning - In a Nutshell

SL - Regularization - Non-Linear Models and Structural Risk Minimization

SL - Regularization - Geometry of L2 Regularization

SL - Regularization - Weight Decay and L2

SL - Regularization - Geometry of L1 Regularization

SL - Regularization - Bayesian Priors

SL - Regularization - Early Stopping

SL - Regularization - Other Regularizers

SL - Regularization - Lasso Regression

SL - Regularization - Lasso vs. Ridge

SL - Regularization - Ridge Regression

SL - Regularization - Introduction

SL - Regularization - Elastic Net and regularized GLMs

SL - Information Theory - Information Theory for Machine Learning

SL - Information Theory - Joint Entropy and Mutual Information II

SL - Information Theory - Joint Entropy and Mutual Information I

SL - Information Theory - KL Divergence

SL - Information Theory - Cross Entropy and KL

SL - Information Theory - Differential Entropy

SL - Information Theory - Entropy II

SL - Information Theory - Entropy I

I2ML - Evaluation - In a Nutshell

SL - Advanced Risk Minimization - Properties of Loss Functions

SL - Advanced Risk Minimization - Bias-Variance Decomposition

SL - Advanced Risk Minimization - MLE vs ERM II

SL - Advanced Risk Minimization - MLE vs ERM I

I2ML - Neural Networks - In a Nutshell

I2ML - Random Forests - In a Nutshell

I2ML - CART - In a Nutshell

I2ML - Supervised Classification - In a Nutshell

I2ML - Supervised Regression - In a Nutshell

I2ML - ML Basics - In a Nutshell

SL - Feature Selection - Wrapper methods

SL - Feature Selection - Filter methods (Examples and caveats)

Advanced Machine Learning - Online learning - Simple learning algorithms

Advanced Machine Learning - Online learning - Online Convex Optimization 2

Advanced Machine Learning - Online learning - Online Convex Optimization 1

Advanced Machine Learning - Online learning - Follow the regularized leader

SL - Feature Selection - Introduction

SL - Feature Selection - Motivating examples

SL - Feature Selection - Filter methods

Advanced Machine Learning - Online learning - Follow the Leader on OQO problems

Advanced Machine Learning - Online learning - Follow the leader on OLO problems

SL - Gradient Boosting - Advanced CWB

SL - Gradient Boosting - Component-Wise-Boosting Basics 2

SL - Gradient Boosting - CWB and GLMs

SL - Gradient Boosting - Component-Wise-Boosting Basics 1

Advanced Machine Learning - Online learning - Introduction

Advanced Machine Learning- Multi-target prediction - Loss Functions