    Course module   NWIIBI008  Category   BA (Bachelor)  Language of instruction   English  Offered by   Radboud University; Faculty of Science; Informatica en Informatiekunde;  Lecturer(s)     Academic year   2021   Period   KW1KW2  (06/09/2021 to 30/01/2022) 
 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    
     
At the end of the course you will be able to
 reason and argue which data mining algorithm is applicable to which task;
 apply, analyze, and implement various data mining algorithms;
 evaluate the quality of data mining solutions.


How can we build systems that can learn? More specifically: how can we extract relevant, interesting information from (big) data? You learn that there are various algorithms, depending on the task at hand and properties of the available data. In the project, you will implement and/or test such algorithms on existing data.
Instructional Modes

 
You
 are uptodate with elementary concepts from probability theory such as probabilities, probability distributions, and expectations;
 can apply these concepts for basic calculations;
 know and understand vectors and matrices;
 can add and multiply those. This prior knowledge is treated in the courses Calculus and Probability Theory and Matrix Calculation


Grading is based upon a midterm exam (35%), an endterm exam (35%), and a project (30%). Homework assignments are mandatory and a sufficient grade is needed to pass the course. A single resit exam replaces both midterm and endterm exams and then counts for 70%.

 


   Recommended materialsBookThe course is originally based on the first edition of the book (which also can be found online in pdf), but moves more and more in the direction of the second edition. 
Title  :   Introduction to Data Mining 
Author  :   Tan, Steinbach, (Karpatne, )and Kumar 
Publisher  :   Pearson 
Edition  :   2 


Instructional modesCourse occurrence
 Practical computer training

 TestsFinal gradeTest weight   1 
Opportunities   Block KW2, Block KW3 
 Digital MidtermTest weight   0 
Test type   Digital exam with CIRRUS 
Opportunities   Block KW1 
 Digital ExamTest weight   0 
Test type   Digital exam with CIRRUS 
Opportunities   Block KW2, Block KW3 
 ProjectTest weight   0 
Opportunities   Block KW2, Block KW3 


  
 
 