Module designation | Artificial Neural Networks (MAT 404) | ||||||||||||||
Semester(s) in which the module is taught | 6th | ||||||||||||||
Person responsible for the module | Auli Damayanti, M.Si. | ||||||||||||||
Language | Indonesian | ||||||||||||||
Relation to curriculum | Compulsory / elective / specialisation | ||||||||||||||
Teaching methods | Lecture and lesson. | ||||||||||||||
Workload (incl. contact hours, self-study hours) | 3×170 minutes (3×50 minutes lecture and lesson, 3×60 minutes structural activities, 3×60 minutes self-study) per week for 16 weeks | ||||||||||||||
Credit points | 3 CP (4,8 ECTS) | ||||||||||||||
Required and recommended prerequisites for joining the module | Algorithms and Programming (MAT101) | ||||||||||||||
Module objectives/intended learning outcomes | General Competence (Knowledge)
Specific Competence: Students are able to
| ||||||||||||||
Content | Basic concepts of data mining, data preprocessing, classification methods, cluster analysis, associations, data mining applications | ||||||||||||||
Examination forms | Essay test (quiz, middle exam) and group presentations (final exam) | ||||||||||||||
Study and examination requirements | Students are considered to pass if they at least have got a final score 40 (D). Final score is calculated as follows: 10%soffskill + 12% assignment + 13% Quiz + 15% midterm + 50% final exam .
Final index is defined as follow:
| ||||||||||||||
Reading list | 1. Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, 3rd Edition, Morgan Kaufmann Publisher, 2012. 2. Pang Ning Tan, Michael Steinbach, dan Vipin Kumar, Introduction to Data Mining, Addison Wesley, 2006. 3. Dan A. Simovichi dan Chabane Djeraba, Mathematical Tools for Data Mining, Springer, 2008 |