Machine learning merges statistics with the computational sciences :computer science, systems science and optimization. Much of the agenda in machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamical and heterogeneous, and where mathematical and algorithmic creativity are required to bring statistical methodology to bear.
Statistics a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data. Statistics itself also provides tools for prediction and forecasting the use of data and statistical models.
Parts of statistics which are extensively use in Machine Learning :
1) Variables
2) Central Tendency
3) Variability
4) Probability
5) Probability Distribution
a) Discrete Probability Distribution
b) Continuous Probability Distribution
6) Hypothesis Testing
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