Course title
情報工学特別講義(データマイニング)   [Dedicated Lecture on Information Engineering]
Course category	technology speciality courses,ets.	Requirement		Credit	2
Department		Year	3～4	Semester	3rd
Course type	3rd	Course code	025811
Instructor(s)
藤本 浩司, 柴原 一友, 藤田 桂英, 宮武 孝尚   [FUJIMOTO Kohji, SHIBAHARA Kazutomo, FUJITA Katsuhide, MIYATAKE Takahisa]
Facility affiliation	Graduate School of Engineering	Office		Email address

Course description

Learning concept and basic skill of data mining, which extracts knowledge from big data. The lecture of this course has the first hand experiences on technology management in various corporates, and will describe actual examples from industry for case-studies, analysis and discussion.

Expected Learning

Learning concept and basic skill in extraction knowledge from big data. See the Curriculum maps.

Course schedule

Day 1(#1-#5) Data mining and cunsumption society, How to predict the future by using possibility, Basic operation of R (statistical analysis software), Report Day 2(#6-#10) What is classification tree?, How to evaluate classification trees, Linear regression, Logistic regression, Non-parametric regression Day 3(#11-#15) Neural network, Support vector machine, Probabilistic-structured model (Bayes' theorem), Report

Prerequisites

In addition to 30 hours that students spend in the class, students are recommended to prepare for and revise the lectures, spending the standard amount of time as specified by the University and using the lecture handouts as well as the references specified below.

Required Text(s) and Materials

Handout and Moodle

References

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management

Assessment/Grading

Evaluate by contributions during the classes(50%) and assignments(50%)

Message from instructor(s)

Course keywords

Classification tree, Linear regression, Logistic regression, Non-parametric regression Neural network, Support vector machine, Bayes' theorem, R (statistical analysis software)

Office hours

Remarks 1

Remarks 2

Related URL

Lecture Language

Language Subject

Last update

6/11/2019 11:00:16 AM