This course deals with the data mining that obtains some knowledge from large amounts of unstructured data, so-called big
data. Students learn about association rules, decision trees, cluster analysis, regression analysis, self-organizing maps,
neural networks, etc. It also enhances the development of students’ skill in carrying out an artificial intelligence (i.e.,
weak AI) with data mining tools. As for machine learning, students learn the characteristics of supervised learning, unsupervised
learning, and reinforcement learning, and develop the ability to select appropriate methods when solving problems. Students
learn the basic data mining technology through interactive training using the data mining software “R”.
The purpose of this class is to acquire basic data mining techniques that are useful for forming a reasonable judgment from
big data. Students learn how to cleanse raw data, how to apply AI, and how to make an reasonable judgment from analysis. Students
use real world's data such as medical and health, e-mail, and purchasing history at supermarkets. Students will be able to
compare what you feel intuitively with what you know from real worlds' data. At the end of the course, students are expected
to acquire a skill to use R for actual data mining.
- At the end of the course, students are expected to acquire a skill to cleanse raw data with R.
- At the end of the course, students are expected to acquire a skill to apply an appropriate AI to each data.
- At the end of the course, students are expected to acquire a skill to make an reasonable judgment from analysis.
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Introduction | Read syllabus | 30minutes |
| 2. | Set up and basic operation of R | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 3. | Data cleansing with R | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 4. | Supervised learning#1 (linear regression) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 5. | Supervised learning#2 (logistic regression) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 6. | Supervised learning#3 (SVM) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 7. | Supervised learning#4 (decision trees) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 8. | Supervised learning#5 (naive bayes) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 9. | Supervised learning#6 (neural networks) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 10. | Unsupervised learning#1 (K-means) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 11. | Unsupervised learning#2 (PCA) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 12. | Unsupervised learning#3 (SOM) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 13. | Unsupervised learning#4 (associations rules) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| 14. | Reinforcement learning#1 (Q leaning) | Make good preparations by reading of the corresponding chapter in the slide. | 250minutes |
| Total. | - | - | 3280minutes |
| Reports | Total. | |
|---|---|---|
| 1. | 30% | 30% |
| 2. | 30% | 30% |
| 3. | 40% | 40% |
| Total. | 100% | - |
Your overall grade in the class will be decided based on the report :
- Cleanse raw data appropriately: 60%
- Apply AI correctly: 70%
- Make an reasonable judgment from analysis: 80%
- Cleanse raw data appropriately: 60%
- Apply AI correctly: 70%
- Make an reasonable judgment from analysis: 80%
This class does not use a text book.
I will hand out an electrical file of teaching materials.
I will hand out an electrical file of teaching materials.
- Course that cultivates an ability for utilizing knowledge
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| Applicable | I was working at the largest telecommunication company in Japan and I was responsible for data mining. Therefore, I will be able to teach practical approach of data mining at the class. |
Last modified : Wed Jun 15 04:04:00 JST 2022