| Course title | |||||
| Applied Physics Laboratory [Applied Physics Laboratory] | |||||
| Course category | Requirement | Credit | 1 | ||
| Department | Year | ～ | Semester | YearLong | |
| Course type | YearLong | Course code | WISE1003 | ||
| Instructor(s) | |||||
| 篠原 俊二郎, 西田 浩之 [SHINOHARA Shunjiro, NISHIDA Hiroyuki] | |||||
| Facility affiliation | University Research Administration Center | Office | Email address | ||
| Course description |
|
Classcode: arxg62p Due to the rapid progress of measuring equipment and communication technology, we can now obtain huge amounts of data in a variety of areas ranging from natural phenomena to the social activities of human beings. Data science systematizes methodologies for extracting valuable information from these vast amounts of data, and related fields cover a wide range, including mathematical statistics, information science, machine learning, and information visualization. The purpose of these lectures is for beginners in data science to learn the methodologies of the field by gaining a broad overview of everything from data preprocessing to the basics of machine learning (both unsupervised and supervised). This class also introduces the latest trends in machine learning in order to direct students' interest toward the ever-evolving technology of data science. This course starts in the last semester. Students need to take this course in the same semester of Data Science Exercise. |
| Expected Learning |
|
・Understand the basics of data science ・Understand the basic methods of machine learning (unsupervised and supervised) |
| Course schedule |
|
The course will be offered almost every other Saturday afternoon from October 21 to December 16. (held on alternate weeks with the " for Data Science Exercise") Classroom: Koganei L1342, 4th floor, Build.13 Session 1-2: Saturday, October 21 (13:00-14:30, 14:45-16:15) 1st session) Introduction: Data science and machine learning 2nd session) Data science fundamentals: Data Acquisition Sessions 3-4 Saturday, November 4 (13:00-14:30, 14:45-16:15) 3rd session) Data science fundamentals: Data preprocessing 4th session) Unsupervised learning: Clustering, dimensionality reduction Sessions 5-6 Saturday, December 2 (13:00-14:30, 14:45-16:15) 5th session) Supervised learning: Linear regression 6th session) Supervised learning: Support vector machines, decision trees Sessions 7-8 Saturday, December 16 (13:00-14:30, 14:45-16:15) 7th session) Supervised learning: Neural networks 8th session) Future of data science |
| Prerequisites |
|
Required elective for Master students and transferred Doctoral students Must have mastered fundamentals of linear algebra and mathematical statistics. |
| Required Text(s) and Materials |
| Handed out when appropriate. |
| References |
| Introduced as appropriate. |
| Assessment/Grading |
|
General evaluation based on grades on mini-tests during lectures and report assignments. |
| Message from instructor(s) |
| It is hoped that students will master the basics of data science, and put the techniques to use in actual research. |
| Course keywords |
| Data processing, machine learning |
| Office hours |
| As appropriate |
| Remarks 1 |
| Remarks 2 |
| Related URL |
| Lecture Language |
| English |
| Language Subject |
| Last update |
| 8/18/2023 3:25:34 PM |