Course title
Topics in Data Engineering

KIMURA Masaomi Click to show questionnaire result at 2018
Course content
While data can be found everywhere in everyday situations, it is not easy to extract useful information and utilize it. Students will understand representative methods of data mining and text mining which are attracting attention as methods of extracting information from data. Students will read related papers in turn, study basic knowledge, and understand the latest trends in research.
Purpose of class
To learn about the foundation of data engineering, including data-mining and text-mining
Goals and objectives
  1. Students can understand the fundamental techniques of data-mining
  2. Students can understand the fundamental techniques of text-mining.
  3. Students can understand and explain academic papers related to data mining and text mining.
Relationship between 'Goals and Objectives' and 'Course Outcomes'

Presentation Minutes paper Total.
1. 20% 10% 30%
2. 20% 10% 30%
3. 30% 10% 40%
Total. 70% 30% -
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. Data mining method (6) SVM Read distributed materials 190minutes
8. Data mining method (7) Deep learning Read distributed materials 190minutes
9. Text mining method (1) Basics: Natural Language Processing Read distributed materials 190minutes
10. Text mining method (2) Text mining techniques and deep learning 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
Evaluation method and criteria
Minutes paper is required for each session.
Students are required to make a presentation of a journal paper related to topics dealt with in this class.
We score 20% for the validity of the paper choice, 20% for a device for the presentation, 40% for the understanding of contents and 20% for discussion.

If the presentation dealt with a suitable paper and its explanation correctly catch the point, it will be evaluated as 60 points.
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
Feedback in the class
Textbooks and reference materials
Statistics, Natural language processing, Database
Office hours and How to contact professors for questions
  • 13:00-14:30 on Friday in Laboratory Room 13-O-32 (Toyosu Campus)
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates an ability for utilizing knowledge
Active-learning course
More than one class is interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
Education related SDGs:the Sustainable Development Goals
    Last modified : Sat Feb 17 04:04:56 JST 2024