Course Jukebox

Course Jukebox

Course Detail

Standard Academic Year
1, 2
Course delivery methods
Computer Science, Engineering & technology
Degree Programs in Systems and Information Engineering (Master's Programs)
Master's Program in Risk and Resilience Engineering
Tsukuba Campus
Course Offering Year
Course Offering Month
October - December
Weekday and Period
Course Number

Data Mining University of Tsukuba

Course Overview

Data analysis techniques in data mining based on knowledge discovery from aspects of statistical learning and machine learning will be the main focus of discussion in this class. The goal of this class is to understand advanced methodologies in data analysis and to apply data mining techniques based on the data analysis techniques actually used in society. Specifically, this class discusses methods for expressing uncertainty inherent in data, exploratory data analysis methods, recent problems in data analysis, and advanced methods corresponding to them.

Learning Achievement

Students will understand data analysis techniques in data mining based on knowledge discovery from statistical and machine learning.
1. Students will understand how to express uncertainty inherent in data 2. Students will understand exploratory data analysis techniques 3. Students will understand current issues in data analysis and emerging methods to address them.


In the general-purpose competence of the degree program, this class is related to competence "1. Ability to utilize knowledge." In the degree program specialized competence, this class is related to competences, "1. Basic engineering skills", "2. Knowledge of basic theory and related technology", "3. Knowledge of practical problems", and "4. Broad perspective". In graduate school competence, this class is related to competences "1. Research ability" and "2. Specialized knowledge."

Course prerequisites

Grading Philosophy

Method of Evaluation: Final report
Ratio: All of the evaluations will be done in the final report (100%). A grade of 60% or higher will be required to pass this class.
Criterion of evaluation: According to the main issue of the report assignment, whether the report can be written correctly and clearly will be the evaluation point.

Course schedule

This class will be given face to face and online with further details of the class posted on the Manaba system. For information on the first class, the details will be informed through this class course on Manaba, so please access the Manaba system.
What is data mining
Multidimensional theory
Multidimensional theory and its applications
Machine learning
Machine learning and its applications
Statistical learning
Statistical learning and its applications
Symbolic data analysis
Symbolic data analysis and its applications
Data fusion theory

Course type


Online Course Requirement


Sato-Ilic Mika

Other information

Fundamental knowledge in Mathematics

Site for Inquiry

Link to the syllabus provided by the university