Introduction
This course introduces the basics of statistics and machine learning in the context of Python. It covers:
- Inferential statistics, such as the bootstrap, p-values, and confidence intervals.
- Methods for constructing and evaluating statistical estimators.
- The fundamentals of the two most important regression models: Linear regression and logistic regression.
Additionally, the course introduces students to the Python packages NumPy, SciPy, and statsmodels
Course content
The course covers the following topics:
- NumPy and SciPy.
- Statistical simulation in Python.
- Exploratory data analysis.
- Statistical models and the bootstrap.
- Unbiased estimators and the efficiency of estimators.
- Construction of estimators.
- Confidence intervals.
- Hypothesis tests and p-values.
- The t-test.
- Foundations of machine learning.
- Linear regression.
- Inference for linear regression.
- Linear regression using categorical covariates (ANOVA).
- Binary regression, such as logistic regression.