Advanced Machine Learning

2018-2019
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 completion
of 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 without
being 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 equivalent
courses.

Literatuur

Slides and additional material that will be posted on Canvas.

Doelgroep

mBA, mBA-D, mMath, mSFM, mCS

Algemene informatie

Vakcode XM_0010
Studiepunten 6 EC
Periode P1
Vakniveau 400
Onderwijstaal Engels
Faculteit Faculteit der Bètawetenschappen
Vakcoördinator prof. dr. S. Bhulai
Examinator prof. dr. G.M. Koole
Docenten prof. dr. S. Bhulai

Praktische informatie

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Werkvormen Hoorcollege
Doelgroepen

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