11:08

TSA - ARIMA Introduction

06:19

GAM - Penalized Least Squares

08:57

GAM - Splines - Natural Cubic Spline, Smoothing Splines

07:54

GAM - Splines - Intro (Polynomials, Piecewise Polynomials, Splines)

08:01

GLM vs. GAM - Generalized Additive Models

08:44

Regression Diagnostics (2/2) - Generalized Linear Models - Residuals, QQ-plot, Outliers

09:58

Regression Diagnostics (1/2) - Linear Models - Residuals, QQ-plot, Outliers

11:15

GLM - Multinomial Regression (3/3) - Ordinal Data (Cumulative Link)

10:02

GLM - Multinomial Regression (2/3) - Nominal Data (Baseline Category)

04:23

GLM - Multinomial Regression (1/3) - Intro

17:35

Is war a war crime? (Israel-Hamas war 6 months analysis)

06:44

Trees - Weights and Feature Importance (Theory + Code)

09:04

Accelerated Failure Time (AFT) vs. Cox Proportional Hazards (CoxPH)

08:36

Accelerated Failure Time (AFT)

05:08

2 Examples - Mixed vs. Regular Models

08:45

Cost Complexity Pruning (Theory + Code)

05:17

Build a Decision Tree from scratch using Python (numpy)

08:35

Decision Trees - Stop Criteria, Categorical Data, NA's, Implementation

07:59

Decision Trees - Split Criteria

04:53

Decision Trees

06:22

Quantile Regression - Numerical Solutions

05:41

Quantile Loss

06:11

Linear vs. Quantile Regression

08:21

Israel vs. Palestine - The October 7 Massacre

12:48

R vs Python - 25 Coding Differences

04:25

Survival Analysis - Cox PH - Breslow Estimator

06:08

Survival Analysis - Cox PH - Partial Likelihood

05:18

Survival Analysis - Cox Proportional Hazards

02:40

Exploratory FA Code in R (psych)

02:51

CFA - Code Example in R (lavaan)

03:33

SEM - Code example in R (lavaan package)

08:21

SEM - Structural Equations Modelling

07:30

EFA vs. CFA - Exploratory vs. Confirmatory Factor Analysis

08:32

Factor Analysis - Statistical Intro 3 - Rotation, cov vs. cor, improper solution & the WOW criteria

11:10

Factor Analysis - Statistical Intro 2 - Estimation: PCA and ML

10:17

Factor Analysis - Statistical Intro 1 - Linear Model, Orthogonality

04:47

Clustering - Hopkins Statistic - Proof

08:33

Clustering - Hopkins Statistic - Definition and Code (Python)

07:44

NN - 26 - SGD Variants - Momentum, NAG, RMSprop, Adam, AdaMax, Nadam (NumPy Code)

22:29

NN - 25 - SGD Variants - Momentum, NAG, RMSprop, Adam, AdaMax, Nadam (Theory)

13:26

NN - 22 - Batch Normalization - Derivatives and Inference

18:09

NN - 21 - Batch Normalization - Theory

16:52

NN - 20 - Learning Rate Decay (with PyTorch code)

13:57

NN - 24 - Activations - Part 2: ReLU Variants

11:51

NN - 23 - Activations - Part 1: Sigmoid, Tanh & ReLU

06:11

NN - 10 - Cross Entropy and Softmax - Derivatives

16:14

NN - 9 - Cross Entropy and Softmax

17:56

NN - 19 - Weight Initialization 2 - What to do? Xavier Glorot & Kaiming He inits

09:44

NN - 18 - Weight Initialization 1 - What not to do?

14:32

NN - 17 - Dropout (Theory + @PyTorch code)

07:10

NN - 16 - L2 Regularization / Weight Decay (Theory + @PyTorch code)

13:55

NN - 15 - Generalization and the Bias-Variance tradeoff (PyTorch code)

14:50

NN - 14 - Generalization and the Bias-Variance tradeoff (Theory)

26:17

Variational Auto Encoder (VAE) - Theory

18:01

Multicollinearity and VIF (theory + R code)

19:01

TSA - Exponential Smoothing - Trend and/or Seasonality (Holt Winters) (theory + R code)

15:56

TSA - Simple Exponential Smoothing - SES (theory + R code)

12:38

TSA - Residuals and Prediction Intervals (theory + R code)

08:13

TSA - Baseline Forecasting (theory + R code)

14:16

TSA - Classical Decomposition (theory + R code)