NWI-NM048D
CDS: Machine Learning
Course infoSchedule
Course moduleNWI-NM048D
Credits (ECTS)3
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Science; Wiskunde, Natuur- en Sterrenkunde;
Lecturer(s)
Coordinator
prof. dr. H.J. Kappen
Other course modules lecturer
Lecturer
prof. dr. H.J. Kappen
Other course modules lecturer
Contactperson for the course
prof. dr. H.J. Kappen
Other course modules lecturer
Examiner
prof. dr. H.J. Kappen
Other course modules lecturer
Academic year2021
Period
KW1  (06/09/2021 to 07/11/2021)
Starting block
KW1
Course mode
full-time
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesYes
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims
The aim of the course is to familiarize the student with the modern concepts of machine learning at the international research level. In particular:
  • The student understands the concepts of Bayesian inference and use it to derive a number of different machine learning methods, such as (multi-layered) perceptrons, graphical models, Markov models and clustering and derive learning methods for these models. 
  • The student is capable to write computer programs to implement the above methods
Content
The course provides an advanced introduction to the modern view on machine learning with emphasis on the Bayesian perspective. The course is intended for Master's students in physics and mathematics as well as master's students in artificial intelligence/computer science with sufficient mathematical background. 
For physics and math students, this course is the follow-up of the bachelor course Inleiding Machine Learning. The course provides a good preparation for a Masters' specialisation in Theoretical Neuroscience or Machine Learning and is part of the minor Computational Data Science. See http://www.snn.ru.nl/~bertk/machinelearning/ for detailed course description.
Level

Presumed foreknowledge
The following courses are useful but not required: Inleiding Machine Learning.
Test information
Examination is weighted average of homework assignments http://www.snn.ru.nl/~bertk/machinelearning
Specifics
 
Required materials
Book
David MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University press. The entire book can be downloaded for free at http://www.inference.phy.cam.ac.uk/mackay/itila/book.html
Handouts
Several handouts will be distributed during the course

Instructional modes
Course occurrence

Lecture

Tutorial

Tests
assignments
Test weight1
Test typeAssignment
OpportunitiesBlock KW1, Block KW2