Introduction to Machine Learning
In this course you will be introduced to basic principles in the field of machine learning, good practice, as well as some important and easily accessible methods. In exercises you get to experiment with these methods and learn how to use them in practice.
Course contents: The course addresses the question how to enable computers to learn from past experiences. It introduces the field of machine learning describing a variety of learning paradigms, algorithms, theoretical results and applications. Ethical and societal aspects of machine learning are discussed. The course covers basic concepts in machine learning and methods such as: nearest neighbour classifier, decision trees, bias and the trade-off of variance, regression, support vector machines, artificial neural networks, ensemble methods, dimensionality reduction, and subspace methods.
The course consists of two elements, theory and practical exercises.
Teaching Format
Teaching consists of lectures.
Assessment
Assessment takes place through written examination, and written and oral presentation of the practical exercises.
Examiner
A list of examiners can be found on
James, Witting, Hastie, Tibshirani and Taylor: Introduction to Machine Learning: With Applications in Python.
The book is available as an e-book via Stockholm University library.
