Introduction to Machine Learning
7.5 credits
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
Roughly 15 minutes with a bike from the city centre, you will find Linnaeus University’s campus. It is like a small society with the university, student accommodation, and student life. Here you become part of a creative knowledge environment.
What will you come across on an excursion in Växjö – the city of contrasts? You will find good restaurants, a celebrated hockey team, and cozy cafés where you can enjoy a latte with lingonberry flavour. In Växjö, beautiful nature is always just around the corner; the city is surrounded by lakes and forests. Students like the combination of the city centre and the active student life on campus. Your dream of the future starts here!
