Statistical and Probabilistic Foundations of AI

Build a Strong Foundation in Fundamental Concepts of AI with our MOOC! 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. Enroll now to strengthen your statistical skills for a career in AI!

• describe data using summary statistics
• construct appropriate statistical plots to visualise information in data
• formulate and analyse stochastic models that describe random processes
• use basic probabilistic tools and methods to extract information from stochastic models
• apply probabilistic methods
• understand the outcomes of basic inferential methods
• construct point estimators (e.g., maximum likelihood estimators), confidence intervals, and hypothesis tests
• make predictions using regression models as well as evaluate the goodness of fit of the regression model
• use R to work efficiently with data

• You will need an introductory knowledge of calculus and linear algebra. You will also need an R installation on your computer (optionally with an integrated development environment (IDE) like Rstudio). Basic knowledge of R is desirable.