The 'Statistical and Probabilistic Foundations of AI' course provides an accessible overview of the mathematics and statistics behind fundamental concepts of machine learning, data science, and artificial intelligence.
The 'Statistical and Probabilistic Foundations of AI' course provides an accessible overview of the mathematics and statistics behind fundamental concepts of machine learning, data science, and artificial intelligence.
It covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. Starting with summary statistics, it focuses on visualising data and the resulting key characteristics. This includes box plots, histograms, kernel density estimates, and regression. In addition, the course provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level. Starting with the basic concepts of probability and elementary stochastic models, the course also covers more advanced topics of probability theory. These include multivariate distributions, generating functions, limit theorems, and a brief introduction to stochastic simulation.
Finally, a brief introduction to inferential statistics is given. Parametric and non-parametric inferential approaches are discussed. Point and interval estimation and hypothesis testing are also covered.
The presentation is rounded off with many examples and data that are analysed and visualised using R.
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