Maskinlæring

1. Upon completion of the course, students will be expected to: 2. Have a good understanding of the fundamental issues and challenges of machine learning: data, model selection, model complexity, etc. 3. Have an understanding of the strengths and weaknesses of many popular machine learning approaches. 4. Have an understanding of the underlying mathematical relationships within and across Machine Learning algorithms and the paradigms of supervised and un-supervised learning. 5. Be able to design and implement various machine learning algorithms in a range of real-world applications.

Lectures and exercises together covering the entire course. Other supporting material (video, text, etc.) may be used. Mandatory assigments: None