Course Schedule 2016

There are two weekly lectures: wednesday 15.15-17.00 hrs and friday 9.00-10.45 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 17.15-19.00 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