    Course module   NWINM048D  Category   MA (Master)  Language of instruction   English  Offered by   Radboud University; Faculty of Science; Wiskunde, Natuur en Sterrenkunde;  Lecturer(s)     Academic year   2020   Period   KW1  (31/08/2020 to 01/11/2020) 
 Starting block   KW1  
 Course mode   fulltime  
 Remarks     Registration using OSIRIS   Yes  Course open to students from other faculties   Yes  Preregistration   No  Waiting list   No  Placement procedure    
     
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 (multilayered) 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


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 followup 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. 
  The following courses are useful but not required: Inleiding Machine Learning. 
 Examination is weighted average of homework assignments http://www.snn.ru.nl/~bertk/machinelearning 
 Examination is weighted average of homework assignments http://www.snn.ru.nl/~bertk/machinelearning 



   Required materialsBookDavid 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 
 HandoutsSeveral handouts will be distributed during the course 


Instructional modesCourse occurrence
 Lecture
 Tutorial

 TestsTentamenTest weight   1 
Opportunities   Block KW1, Block KW2 


  
 
 