Algemene informatie
Vakcode | XM_0010 |
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Studiepunten | 6 EC |
Periode | P1 |
Vakniveau | 400 |
Onderwijstaal | Engels |
Faculteit | Faculteit der Bètawetenschappen |
Vakcoördinator | prof. dr. S. Bhulai |
Examinator | prof. dr. S. Bhulai |
Docenten |
prof. dr. S. Bhulai |
Praktische informatie
Voor dit vak moet je zelf intekenen.
Voor dit vak kun je last-minute intekenen.
Werkvormen | Hoorcollege |
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Doelgroepen
Dit vak is ook toegankelijk als:
Dit vak wordt in het Engels aangeboden. Omschrijvingen kunnen daardoor mogelijk alleen in het Engels worden weergegeven.
Doel vak
There are several learning objectives for this course. After completionof this course, the student should be able to:
1. understand the capabilities and the limitations of machine learning,
2. implement machine learning algorithms in Python,
3. know relevant machine learning algorithms for both supervised and
unsupervised learning problems,
4. select the right machine learning models for real-world use cases,
5. understand when to apply online learning, reinforcement learning, and
deep learning,
6. interpret the outcomes of machine learning algorithms.
Inhoud vak
Machine learning is the science of getting computers to act withoutbeing explicitly programmed. Machine learning is so pervasive today that
it is used in everyday life without knowing it. In this course, you will
learn about the most effective machine learning techniques, and gain
practice implementing them and getting them to work yourself. We will
discuss the theoretical underpinnings as well as the practical know-how
needed to apply these techniques to new problems.
Onderwijsvorm
Lectures, guest speakers, and tutorials.Toetsvorm
Tutorial assignments and a written exam.Vereiste voorkennis
Linear Algebra (X_400042 ) and Statistics (X_400004) or equivalentcourses.