Course Schedule 2016
There are two weekly lectures: wednesday 15.1517.00 hrs and friday 9.0010.45 hrs. Furthermore, there are computer lab sessions in which we practice with R, and work on the practical assignments. These sessions take place on wednesday 17.1519.00 hrs. In the schedule below, lecture 38A is the lecture on wednesday in week 38, 38B is the lecture on friday in week 38, and 38C is the computer lab session in week 38. The first lecture is on friday, september 9th. The first computer lab session is on wednesday, september 14th.
Lecture  Subject/Slides  Literature 

36B  Introduction  A. Feelders et al.: Methodological and Practical Aspects of Data Mining 
37A  Classification Trees (1) Short Intro to Entropy (optional) 
Lecture Notes on Classification Trees 
37C  Computer Lab: Introduction to R Work on assignment 1 

37B  Classification Trees (2)  Lecture Notes on Classification Trees 
38A  Classification Trees/Exercise Class  Exercises Solutions 
38C  Computer Lab: Work on assignment 1  
38B  Undirected Graphical Models (1)  Lecture Notes on Graphical Models 
39A  Undirected Graphical Models (2)  Lecture Notes on Graphical Models 
39C  Computer Lab: Work on assignment 1  
39B  Undirected Graphical Models Exercise Class  Exercises (Solutions). Extra exercise and solution. 
40A  Frequent Pattern Mining (1)  Lecture Notes on Frequent Item Set Mining 
40C  Computer Lab: Work on assignment 1  
40B  Frequent Pattern Mining (2)  
41A  Frequent Pattern Mining Exercise Class  Exercises Solutions 
41C  Computer Lab: Work on assignment 1  
41B  Bayesian Networks (1)  Lecture Notes on Bayesian Networks 
42A  Bayesian Networks (2)  Lecture Notes on Bayesian Networks 
42C  Computer Lab: Work on assignment 2  
42B  Exercise Class Bayesian Networks  Exercises Solutions 
43A  Bayesian Network Classifiers  Article Friedman et al. 
43C  Computer Lab: Work on assignment 2  
43B  Social Network Mining  Article Lu and Getoor 
44A  No Lecture  
44C  Computer Lab: Work on assignment 2  
44B  No Lecture  
45  Written Exam 