Introduction to Machine Learning

The course covers machine learning concepts and methods. The following topics are covered in the course:

• basic statistical concepts

• supervised and unsupervised learning

• linear and polynomial regression,

• logistic regression

• decision trees

• Support vector machines

• unsupervised learning using the k-means clustering algorithm

• algorithm evaluation using cross-validation and mean square error

• evaluation metrics such as precision, recall, and F-score

• algorithm implementation using MATLAB

Växjö

Linnaeus University’s campus is located roughly 15 minutes with bicycle from the city centre. Campus is like a small society in its own right, with the university, student accommodation and student life all in one place. Everything happens on campus – here you become part of a creative knowledge environment and an eventful student life.