| Course title | |||||
| データサイエンス概論 [Outline of Data Science] | |||||
| Course category | Requirement | Credit | 1 | ||
| Department | Year | ～ | Semester | 3rd | |
| Course type | 3rd | Course code | WISE1003 | ||
| Instructor(s) | |||||
| 近藤 敏之 [KONDO Toshiyuki] | |||||
| Facility affiliation | Faculty of Engineering | Office | afjgxte/L1151 | Email address | |
| Course description |
| With the rapid progress of measuring instruments and communication technology, it has become possible to obtain huge data in various fields from natural phenomena to human social activities. Data science is the systematization of the methodology to extract valuable information from such huge data, and the related fields include mathematical statistics, information science, machine learning, information visualization and so on. In this lecture, you can learn the basic methodology of data science by looking through data preprocessing, the theory of machine learning (unsupervised learning, supervised learning). In addition, to focus on the ever-evolving data science technology, we also introduce the latest trends in machine learning technology. |
| Expected Learning |
|
- Understand the basics of data science. - Understand basic machine learning methods (unsupervised learning and supervised learning). See the Curriculum maps. |
| Course schedule |
|
1. Introduction: Data science and machine learning 2. Basics of data science: Data preprocessing (1) 3. Basics of data science: Data preprocessing (2) 4. Unsupervised learning: Clustering, dimension compression 5. Supervised learning: Support Vector Machine, Decision Tree 6. Supervised learning: Neural networks 7. Supervised learning: Deep learning 8. The present and the future of data science |
| Prerequisites |
|
- This lecture is available only for the students of WISE program. - Basics of linear algebra and Statistics. - 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. |
| Required Text(s) and Materials |
| Digital documents are distributed in the lecture. |
| References |
| Digital documents are distributed in the lecture. |
| Assessment/Grading |
|
- Weekly mini-test - Report on the final project |
| Message from instructor(s) |
| Learn the basics of data science and use it for your research. |
| Course keywords |
| Office hours |
| Ask questions at any time by email. |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
| English |
| Language Subject |
| Last update |
| 1/8/2020 11:46:44 AM |