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Cursus: INFOMPR
INFOMPR
Pattern recognition
Cursus informatieRooster
CursuscodeINFOMPR
Studiepunten (ECTS)7,5
Categorie / NiveauM (M (Master))
CursustypeCursorisch onderwijs
VoertaalEngels
Aangeboden doorFaculteit Betawetenschappen; Graduate School of Natural Sciences; Graduate School of Natural Sciences;
Contactpersoondr. A.J. Feelders
Telefoon+31 30 2533176
E-mailA.J.Feelders@uu.nl
Docenten
Contactpersoon van de cursus
dr. A.J. Feelders
Overige cursussen docent
Docent
dr. A.J. Feelders
Overige cursussen docent
Docent
dr. A. Gatt
Overige cursussen docent
Blok
2  (15-11-2021 t/m 04-02-2022)
Aanvangsblok
2
TimeslotD: D (WO-middag, WO-namiddag, Vrijdag)
Onderwijsvorm
Voltijd
Cursusinschrijving geopendvanaf 20-09-2021 t/m 03-10-2021
AanmeldingsprocedureOsiris Student
Inschrijven via OSIRISJa
Inschrijven voor bijvakkersJa
VoorinschrijvingNee
Na-inschrijvingJa
Na-inschrijving geopendvanaf 25-10-2021 t/m 26-10-2021
WachtlijstJa
Plaatsingsprocedureadministratie onderwijsinstituut
Cursusdoelen
After completing this course, the student:

- knows important methods of statistical pattern recognition and their theoretical foundation
- knows general principles of statistical learning, such as over-fitting and the bias-variance decomposition
- knows how to apply methods of statistical pattern recognition to practical data analysis problems
- can perform an experimental evaluation of statistical learning methods in a sound manner
- knows the fundamental deep learning topics
- has practical skills for applying deep learning methods (e.g. feed forward neural networks, CNNs, RNNs)
- can model a research-oriented problem in the application domain of deep learning and suggest a solution using deep learning techniques

Assessment
The course is graded through a practical assignment (20%), a group project (40%) and a written exam (40%).

The repair test requires at least a 4 for the original test.
Inhoud

In this course we study statistical pattern recognition and machine learning.

The subjects covered are:

  • General principles of data analysis: overfitting, bias-variance trade-off, model selection, regularization, the curse of dimensionality.
  • Linear statistical models for regression and classification.
  • Clustering and unsupervised learning.
  • Support vector machines.
  • Neural networks and deep learning.

Knowledge of elementary probability theory, statistics, multivariable calculus and linear algebra is presupposed.

Course form
Lectures and computer lab sessions.

Literature

  • Book: Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
  • Book: Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016.
    Book URL: http://www.deeplearningbook.org.
  • Possibly additional literature in the form of research papers, book chapters, etcetera.
Competenties
-
Ingangseisen
Je moet voldoen aan de volgende eisen
  • Toelatingsbeschikking voor de master toegekend
Verplicht materiaal
Boek
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
Artikelen
Additional literature for the most recent material in the form of research papers, book chapters, etc.
Aanbevolen materiaal
Software
R for Windows (beschikbaar in MyWorkPlace)
Werkvormen
Hoorcollege

Werkcollege

Toetsen
Eindresultaat
Weging100
Minimum cijfer-

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