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Master advanced ML/DS statistics
·
Module 3
Statistical Inference and Estimation
1
Parameters vs. Statistics in Model Training
Distinguish between population parameters and sample statistics in the context of model training.
Confidence Intervals for Means and Proportions
Construct and interpret confidence intervals for a population mean or proportion.
3
Hypotheses for A/B Tests and Model Comparisons
Formulate null and alternative hypotheses for A/B testing and model comparisons.
4
P-Values and Data-Driven Decisions
Calculate p-values from test statistics and use them to make data-driven decisions.
5
Method of Moments for Parameter Estimation
Explain the principle of the method of moments for parameter estimation.
6
Likelihood Function Formulation
Formulate the likelihood function for a given dataset and a probabilistic model.
7
Maximum Likelihood Estimation Explained
Explain the principle of Maximum Likelihood Estimation (MLE).
8
Maximum Likelihood Estimation for Simple Models
Apply MLE to find the parameters of a simple model (e.g., for a Bernoulli or Normal distribution).
Previous module
Probability and Distributions for Modeling Data
Next module
Optimization for Machine Learning