These notes cover the statistical methods that drive data analysis and model evaluation in machine learning:
- Descriptive Statistics: Mean, median, mode, standard deviation, percentiles, and data summarization.
- Hypothesis Testing: Null and alternative hypotheses, p-values, significance levels, Type I and Type II errors.
- Confidence Intervals: Interval estimation, margin of error, and interpreting uncertainty in predictions.
- Regression & MLE: Ordinary least squares, maximum likelihood estimation, bias-variance tradeoff, and model selection.