Course title
7D240000
Intelligent Data Engineering

 kimura masaomi
Course content
While data can be found everywhere in everyday settings, it is not easy to extract effective information and utilize it. Themes relating to data mining methods and text mining methods which are attracting attention as methods of extracting information from data, or themes relating to ontology databases (topic maps/semantic web) and XML and XML databases as methods of applying data will be addressed.
Purpose of class
To learn about the foundation of data engineering, including data-mining, text-mining and XML
Goals and objectives
  1. Learn the foundation of data-mining
  2. Learn the foundation of text-mining.
  3. Learn the foundation of XML.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. Guidance (outlines related to data/text mining techniques) Read distributed materials 190minutes
2. Data mining method (1) Overview of data mining Read distributed materials 190minutes
3. Data mining method (2) Association analysis, memory-based reasoning Read distributed materials 190minutes
4. Data mining method (3) Clustering analysis, genetic algorithm Read distributed materials 190minutes
5. Data mining method (4) Decision Tree analysis, network analysis Read distributed materials 190minutes
6. Data mining method (5) artificial neural network Read distributed materials 190minutes
7. Text mining method (1) Basics: Natural Language Processing Read distributed materials 190minutes
8. Text mining method (2) Text mining techniques and examples Read distributed materials 190minutes
9. XML (1) Basics Read distributed materials 190minutes
10. XML (2) Semantic Web Read distributed materials 190minutes
11. Presentation (1) Prepare a presentation material 190minutes
12. Presentation (2) Prepare a presentation material 190minutes
13. Presentation (3) Prepare a presentation material 190minutes
14. Presentation (4) Prepare a presentation material 190minutes
Total. - - 2660minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Presentation Total.
1. 35% 35%
2. 35% 35%
3. 30% 30%
Total. 100% -
Evaluation method and criteria
Reports relating to reference papers and topics during the term
Students will be evaluated on points they raise regarding papers and topics.
Textbooks and reference materials
Nothing.
Prerequisites
Statistics, Natural language processing, Dataabase
Office hours and How to contact professors for questions
  • 13:00-14:30 on Friday in Laboratory Room 13-O-32 (Toyosu Campus)
Relation to the environment
Non-environment-related course
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
Most classes are interactive
Last modified : Wed Oct 17 08:25:32 JST 2018