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Cursus: KI2V20001
KI2V20001
Introduction to Machine Learning
Cursus informatieRooster
CursuscodeKI2V20001
Studiepunten (ECTS)7,5
Categorie / Niveau2 (2 (Bachelor Verdiepend))
CursustypeCursorisch onderwijs
VoertaalEngels
Aangeboden doorFaculteit Geesteswetenschappen; BA Onderwijs Geesteswetenschappen; Ug School Liberal Arts;
Contactpersoondr. T. Deoskar
E-mailt.deoskar@uu.nl
Docenten
Contactpersoon van de cursus
dr. T. Deoskar
Overige cursussen docent
Docent
dr. P.J. Mosteiro Romero
Overige cursussen docent
Docent
H.G. Schnack
Overige cursussen docent
Blok
4  (25-04-2022 t/m 01-07-2022)
Aanvangsblok
4
TimeslotAD: AD
Onderwijsvorm
Voltijd
Cursusinschrijving geopendvanaf 01-11-2021 09:00 t/m 29-11-2021 23:59
AanmeldingsprocedureOsiris
Inschrijven via OSIRISJa
Inschrijven voor bijvakkersJa
VoorinschrijvingNee
Na-inschrijvingJa
Na-inschrijving geopendvanaf 04-04-2022 09:00 t/m 05-04-2022 23:59
WachtlijstNee
Plaatsingsprocedure(Sub)school
Cursusdoelen
This aim of this course is to introduce students to the basic principles of machine learning,  as well as several of the most common models and algorithms used in machine learning. The main focus of the course will be on supervised machine learning, but some introduction to unsupervised learning methods like clustering (K-means) will also be provided.  Additionally, the course also aims to cover the mathematical concepts most relevant for machine learning, in particular probability theory,  linear algebra, and differential and integral calculus required for this course as well as follow-up courses in machine learning.
Inhoud

This course will  introduce students to the basic principles of machine learning  as well as several of the most common models and algorithms used in machine learning. The course will begin with linear models (classification and regression), ending with more modern methods like artificial neural networks. The main focus of the course will be on supervised machine learning, but some introduction to unsupervised learning methods like clustering (K-means) will also be provided.  Additionally, the course will  cover the mathematical concepts most relevant for machine learning, in particular probability theory, linear algebra, and differential and integral calculus required for this course as well as follow-up courses in machine learning. 
 
Topics: basics concepts of machine learning, K-nearest neighbours, perceptron, linear classification and regression, logistic regression, feedforward neural networks, concepts related to overfitting and validation, gradient descent. 

 
Competenties
-
Ingangseisen
-
Voorkennis
Programming (python)
Knowledge covered in usual trajectory of courses in AI Bachelor (e.g. Wiskunde voor KI)
Verplicht materiaal
Boek
“Learning from data”, Abu-Mostafa, Magnon-Ismail and Lin. Paper edition: Publisher: AMLBook (2012), ISBN-10: 1600490069 ISBN-13: 978-1600490064 E-edition (Kindle): Publisher: AMLBook (2017), ASIN: B0759M2D9H
ISBN:978-1600490064
Titel:Learning from data
Auteur:Abu-Mostafa, Magnon-Ismail and Lin
Uitgever:AMLBook (2012), AMLBook (2017)
Kosten materiaal:37,00
Werkvormen
Hoorcollege

Werkcollege

Toetsen
Opdracht
Weging50
Minimum cijfer5,5

Toets
Weging50
Minimum cijfer5,5

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