01CF109 Data Mining

2.0 Credits, 1, 2 Year, FallAB Tue5,6
Mika Sato-Ilic

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.

Remarks

要望があれば英語で授業

Course Type

lectures

Field

Total Risk Management

Relationship with Major Educational Goals

This lecture is related mainly to "1. The basic theory for analyzing and assessing risk," partly to "3. The real world problems that are the target of risk engineering" and "4. The ability to approach risk engineering from a wide range of perspectives."

Course Highlight

This course will be accomplished both through the understanding of advanced methodologies related with exploratory data analyses in the core area of data analysis based on mathematical arguments and substantial impact from the real world.

Calendar

Outcomes

1) Students should understand the methods for representing latent uncertainty in data.
2) Students should understand exploratory data analysis.
3) Students should understand several recent problems in data analyses and their corresponding advanced methods.

Grading

The evaluation will be based on the reports.

Relationship with Student Achievement Assessment Attributes

For students majoring in this field, the lecture is related mainly to "1) Knowledge of fundamental/basic theory in the major field," and partly to "3) Understanding of real world problems" and "4) Ability in recognizing problems from a broad perspective."
For students majoring in other fields, this lecture is related mainly to "2) Knowledge of fundamental/basic theory of related fields," and partly to "3) Understanding of real world problems" and "4) Ability in recognizing problems from a broad perspective."

Text

References

Office Hour

Prerequisites

Fundamental knowledge in Mathematics

Other Information

None

Related Courses

Self-study

Fundamental knowledge and application of data mining