Machine Learning

2018-2019
Dit vak wordt in het Engels aangeboden. Omschrijvingen kunnen daardoor mogelijk alleen in het Engels worden weergegeven.

Doel vak

The goal of this course is to present the dominant concepts of machine
learning methods including some theoretical background. We'll cover
established machine learning techniques such as Linear Models, Support
Vector Machines, Decision Trees and Neural Networks, as well as some
statistical techniques to assess and validate machine learning results.

Inhoud vak

Machine Learning is the study of how to build computer systems that
learn from experience. It is a very active subfield of Artificial
Intelligence that intersects with statistics, cognitive science,
information theory, and probability theory. Recently, Machine Learning
has gained great importance for the design of search engines, robots,
and sensor systems, and for the processing of large scientific data
sets. Further applications include handwriting or speech recognition,
image classification, medical diagnosis, stock market analysis,
bioinformatics, etc.

Onderwijsvorm

The course consists of two parts: a written exam and a practical
assignment. The written exam is supported by lectures (two per week) and
homework assignments (one per week). The practical assignment is
supported by small exercises to help with the relevant technologies, and
informal biweekly presentations. The practical assignment is made in
groups of five.

The course will be taught in English.

Toetsvorm

Exam (50%) and a project report (50%).

Literatuur

A digital reader will be provided.

Doelgroep

3BA, 3CS, 3LI, 3IMM, mBio

Aanbevolen voorkennis

We recommend that students have some prior experience with Linear
Algebra, Calculus (limited to differentiation), and Probability Theory
(or Statistics). A basic understanding will suffice and we will take
some time to go over the basics again. Feel free to register if you have
no experience with any of these, but expect to put in a little extra
effort in the first weeks.

Programming experience, preferably in python, is highly recommended, but
not strictly necessary.

Algemene informatie

Vakcode X_400154
Studiepunten 6 EC
Periode P4
Vakniveau 300
Onderwijstaal Engels
Faculteit Faculteit der Bètawetenschappen
Vakcoördinator dr. P. Bloem
Examinator dr. P. Bloem
Docenten dr. P. Bloem

Praktische informatie

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Werkvormen Werkcollege, Hoorcollege
Doelgroepen

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