Machine Learning Foundations: Statistics

Introduction

• Foundations of statistics for machine learning
• What you should know

1. Introduction to Statistics

• Defining statistics
• Applications of statistics in ML
• Types of data

2. The Summary Statistics

• The mean
• The median
• The mode
• The percentile
• The percentage change
• The range
• The variance and the standard deviation
• The standard error of the mean vs. the standard deviation

3. From Quantiles to Correlation

• The quantiles and box plots
• Missing data
• The correlation
• The covariance
• The correlation coefficient
• The correlation vs. causation

4. Random Variables and Probability Distribution

• Introduction to probability distribution
• The uniform distribution
• The normal distribution
• The Bernoulli distribution
• The Multinoulli distribution

5. Sampling and Replacement

• Selection with replacement
• Selection without replacement
• Bootstrapping

6. Linear Regression

• Independent and dependent variables
• Linear regression for continuous values
• Fitting a line
• Linear least squares

Conclusion

• Next steps