Machine learning (ML) is at the core of many applications of artificial intelligence. A key goal of this course series is to teach the fundamental building blocks behind supervised ML. In this first part, we will introduce you to the very basics of supervised machine learning and treat concepts such as regression, classification, empirical risk minimization, and learner.

• Introduction to basic concepts of supervised ML such as data, task, model, hypothesis space, learner, empirical risk minimization

• Linear and polynomial regression models, L1 and L2 loss

• Classification models such as logistic regression, discriminant analysis, and Naive Bayes

• Explain the building blocks of supervised ML

• Explain the importance of empirical risk minimization for supervised ML

• Apply linear and polynomial regression models in R or Python, respectively, to tabular data

• Apply classification models such as logistic regression, discriminant analysis, and Naive Bayes in R or Python, respectively, to tabular data

This course is open to all who are interested. However, we recommend learners to have:

• A strong foundation in mathematics, such as 8 years of math education in secondary schools

• Pre-knowledge in linear algebra and analysis required (at least high school level)

• Pre-knowledge in statistics and probability is recommended (at least high school level)

• Basic programming skills in R or Python (e.g., through a small self-study course)