| Course title | |||||
| 知能情報システム工学特別講義（データ分析の数理） [Special Lecture on Electrical Engineering and Computer Science (Data Analysis)] | |||||
| Course category | technology speciality courses | Requirement | Credit | 2 | |
| Department | Year | 3～4 | Semester | 3rd | |
| Course type | 3rd | Course code | 023671 | ||
| Instructor(s) | |||||
| 藤田 桂英, 柴原 一友, 藤本 浩司, 宮武 孝尚 [FUJITA Katsuhide, SHIBAHARA Kazutomo, FUJIMOTO Kohji, 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 firsthand experiences on technology management in various corporates, and will describe actual examples from industry for case-studies, analysis and discussion. Google ClassCode: md56hku |
| 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 |
| 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 |
| 9/21/2022 2:00:02 PM |