Course title
Y02112402
Practice on Data and Science

 YAMAZAWA Hiroshi

HIROSE Sampei
Course description
Learn and practice the mechanism of machine lerning, which is an important role in data science.
Purpose of class
Knowing how machine learning is used in the real world through PBL and getting in touch with it. We are planning a PBL through an internship with a company.
Goals and objectives
  1. You can explain the mechanism of machine lerning.
  2. You can perform data cleansing using actual data.
  3. You can create a model in machine leaining using actual data.
  4. You can make predictions in machine learning using asutual data.
Language
Japanese
Class schedule

 Class schedule HW assignments (Including preparation and review of the class.) Amount of Time Required
1. What is machine learning?1(supervised learning/regression) Preliminary preparation of slide materials 60minutes
Exercises 30minutes
2. What is machine learning?2(supervised learning/classification) Preliminary preparation of slide materials 60minutes
Exercises 30minutes
3. What is machine learning?3(unsupervised learning/Principal component analysis) Preliminary preparation of slide materials 60minutes
Exercises 30minutes
4. What is machine learning?4(unsuperxised learning/K-means clustering) Preliminary preparation of slide materials 60minutes
Exercises 30minutes
5. PBL1 Task 90minutes
6. PBL2 Task 90minutes
7. PBL3 Task 90minutes
Total. - - 630minutes
Relationship between 'Goals and Objectives' and 'Course Outcomes'

 Exercises Task Total.
1. 50% 0% 50%
2. 0% 10% 10%
3. 0% 20% 20%
4. 0% 20% 20%
Total. 50% 50% -
Evaluation method and criteria
A total of 100 pts, 50% of each exercise and 50% of the results of the PBL assignments, will be passed with a score of 60 or higher.
Feedback on exams, assignments, etc.
ways of feedback specific contents about "Other"
The Others 状況に応じてフィードバックを行う
Textbooks and reference materials
Reference book: Python, Rで学ぶデータサイエンス,　Chantal D. Larose & Daniel T. Larose 著, 東京化学同人
Prerequisites
Review the machine learning you will learn in the introduction to data science.
Office hours and How to contact professors for questions
  • 授業後に対応する。またメールにて時間調整する。
Regionally-oriented
Non-regionally-oriented course
Development of social and professional independence
  • Course that cultivates a basic problem-solving skills
Active-learning course
About half of the classes are interactive
Course by professor with work experience
Work experience Work experience and relevance to the course content if applicable
N/A 該当しない
Education related SDGs:the Sustainable Development Goals
  • 4.QUALITY EDUCATION
  • 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
Last modified : Sat Sep 09 07:03:49 JST 2023