2019 Materials Informatics (I)

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Academic unit or major
Tokyo Tech Academy for Convergence of Materials and Informatics
Instructor(s)
Sekijima Masakazu  Kabashima Yoshiyuki  Matsushita Yuichiro  Kosugi Taichi  Yasuo Nobuaki 
Course component(s)
Lecture     
Day/Period(Room No.)
Fri1-4(南4号館 情報ネットワーク演習室 第1演習室, Information Network Exercise Room 1st Exercise Room)  
Group
-
Course number
TCM.A405
Credits
2
Academic year
2019
Offered quarter
4Q
Syllabus updated
2019/11/29
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

In the current society, it is essential in all fields to appropriately exploit "big data" for finding rules and/or making predictions/decisions. This course gives fundamental knowledges and basic skills for handling large-scale data sets with the aid of computers.

Student learning outcomes

Students will be able to apply basic knowledges on statistics for analyzing data and evaluating the obtained results mathematically.

Keywords

classification, clustering, principal component analysis, dimension reduction, training/generalization errors, cross validation

Competencies that will be developed

Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

In each week we give a lecture and an exercise session.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Class guidance Guidance for class flow, computing environment, and used programming language (Python)
Class 2 Prerequirement exam - data mining, and - basic statistics and mathematics for data analysis Check basic knowledge about mathematics and Python language
Class 3 Fundamentals of data analysis Learn basic knowledge about statistics and data science
Class 4 Arrangement of computing environment and warming-up of programming Arrange computing environment and carry out simple excercises of programming
Class 5 Classification and model evaluation Learn methods for extracting discrimination rules from labeled data. Learn about difference between training error and generalization error, and methods of model evaluation.
Class 6 Classification Do exersises on methods for extracting discrimination rules from labeled data
Class 7 Clustering Learn methods for categorizing unlabeled data into several categories
Class 8 Clustering Do exersises on methods for categorizing unlabeled data into several categories
Class 9 Principal component analysis Learn principal component analysis together with mathematical issues related to it
Class 10 Principal component analysis Do exersises on principal component analysis with mathematical issues related to it
Class 11 Dimension reduction Learn methods for dimension reduction such as multidimensional scaling and canonical correlation analysis
Class 12 Dimension reduction Do exersises on methods for dimension reduction such as multidimensional scaling and canonical correlation analysis
Class 13 Advanced topics Learn methods for ensemble learning
Class 14 Advanced topics Do exersises on methods for ensemble learning
Class 15 General discussion Discuss possible applications of data analysis in various fields

Textbook(s)

Not specified

Reference books, course materials, etc.

Distributed via OCW-i

Assessment criteria and methods

Based on reports for given assignments.

Related courses

  • XCO.T489 : Fundamentals of artificial intelligence
  • XCO.T490 : Exercises in fundamentals of artificial intelligence
  • XCO.T483 : Advanced Artificial Intelligence and Data Science A
  • XCO.T484 : FinTech and Data Science
  • XCO.T485 : Advanced Artificial Intelligence and Data Science C
  • XCO.T486 : Advanced Artificial Intelligence and Data Science D
  • TCM.A403 : Materials simulation (I)
  • TCM.A404 : Materials Informatics (R)

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Take a prerequirement exam on "linear algebra", "analysis", and "basic grammar and functions of Python3" in the first class.

Other

Only TAC-MI students can register this course in 2019. Students are required to get Google accounts and to get ready for using functions of "file upload/download" in Google Drive.

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