    Course module   NWINM116B  Category   MA (Master)  Language of instruction   English  Offered by   Radboud University; Faculty of Science; Wiskunde, Natuur en Sterrenkunde;  Lecturer(s)     Academic year   2021   Starting block    Course mode   fulltime  
 Remarks     Registration using OSIRIS   Yes  Course open to students from other faculties   Yes  Preregistration   No  Waiting list   No  Placement procedure    
     
 The student can apply basic statistical data analysis concepts (e.g. probabilities, likelihoods, confidence level, frequentist, bayesian statistics)
 The student understands and can apply basic Machine Learning methods (simple regression, test statistics, training etc.)
 The student can solve classification and regression problems with Machine Learning methods and explain how these methods work
 The student can explain basic concepts of image analysis and is able to apply image analysis algorithms (convolutional networks)
 The student can analyse sequential (temporal) data with the help of recurrent networks
 The student can explain the concepts of generative networks and optimization algorithms and can practically apply such algorithms



This lecture discusses recent developments in Data Science and Machine Learning used in the fields of Particle Physics and Astronomy.
Various practical examples are explained and handson exercises are used to learn the material.
We will use the python programming language. Basic knowledge of python is required.
The course covers the following topics:
 Introduction into statistical data analysis (e.g. probabilities, likelihoods, confidence level, frequentist, bayesian statistics)
 Introduction into basic Machine Learning methods (simple regression, test statistics, training etc.)
 Finding signal events in background events (classification and decision trees)
 Learning a Function (regression and deep networks)
 Image Analysis in Particle Physics and Astronomy (Computer Vision)
 Radio and gravitational wave analysis (Signal Detection in temporal data, recurrent networks)
 Generating Data (Generative Networks)
 Fitting Data (Optimalization and Sampling Algorithms)

  Working knowledge of python programming language. 
 The exam will be in the form of a programming exercise. 
 


   Recommended materialsIn consultation with teacherLecture notes will be made available 
 Sheets 

Instructional modesCourseAttendance Mandatory   Yes 
 Lecture
 TutorialAttendance Mandatory   Yes 
 Zelfstudie

 TestsProgramming ExcerciseTest weight   1 
Test type   Assignment 
Opportunities   Block JAAR 


  
 
 