2019 Materials Informatics (R)

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Academic unit or major
Tokyo Tech Academy for Convergence of Materials and Informatics
Sekijima Masakazu  Kabashima Yoshiyuki  Matsushita Yuichiro  Kosugi Taichi  Yasuo Nobuaki 
Course component(s)
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)  
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
Access Index

Course description and aims

In the current society it is essential in almost all fields to make use of computation and data appropriately. This course gives fundamental knowledge and basic skills for using computers to analyze big data and making use of it.
This course aims to (1) teach foundations of statistics and computation for processing and analyzing a given data set, and (2) help students to understand and apply various computer software tools for data analysis to get new findings.

Student learning outcomes

(1) Students will be able to apply basic statistical knowledge to analyzing data and evaluating the obtained results mathematically.
(2) Students will be able to understand the basis of data processing mechanisms and make use of various data mining software tools appropriately.


data mininig, computation (data processing), knowledge representation, data mining software, statistical test

Competencies that will be developed

Intercultural skills Communication skills Specialist 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 and an instruction of TSUBAME Prepare software enviroments for exercises. Exercise of operating a Linux system.
Class 2 Brief introduction to - computation and programming. Introduction to - data mining, and - basic statistics and mathematics for data analysis Understand the general idea on computation and data mining, and obtain basic knowledges for this course.
Class 3 Exercise: analysis of mushroom data Understand the outline of data mining, and explain what is sample data set.
Class 4 Classification : simple rule and naiive basyesian rule Understand the outline of classification, and explain simple rule and naiive baysesian rule derivations.
Class 5 Exercise: naiive bayesian rule Understand simple classification rule generation mechanism and use the generator appropriately.
Class 6 Classification: decision tree Explain the priciple idea of decision tree construction.
Class 7 Exercise: decsion rule Undersdand the mechanism of decision tree construction algorithm and use the standard decsion tree constructor appropriately.
Class 8 Association rule Explain a concept of association rule and its evaluation method.
Class 9 Exercise: association rule Undersdand the mechanism of association rule generation and use the standard generator appropriately.
Class 10 Regression Undersdand a concept of regression and various evaluation methods.
Class 11 Exercise: regression Undersdand the principle idea of deriving regression rules and use the standard regression rule derivation tools appropriately.
Class 12 Clustering Explain the idea of clustering and basic techniques for clustering.
Class 13 Exercise: clustering Understand the mechanism of standard clustering techniques and use them for identifying clusters.
Class 14 Summary and advanced topics Choose appropriate data mining methods and tools given in this course, and overview advanced topics.
Class 15 Final project Choose an appropriate method for analyzing more practical (but still basic) data mining problems, and evaluate the obtained analysis.


I.H. Witten, E. Frank, M.A. Hall, and C. Pal, Data Mining: Practical Machine Learning Tools and Techniques (The 4th Ed.),
Morgan Kaufmann Pub., 2013, ISBN-13: 978-0128042915

Reference books, course materials, etc.

Weka Web Page, https://www.cs.waikato.ac.nz/ml/weka/

Assessment criteria and methods

Assignments given at each exercise session (90%) and a final report project (10%).

Related courses

  • ART.T458 : Machine Learning

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

No prerequisites.

Office hours

Questions can be sent by email (at any time).


Only TAC-MI students can register this course in 2019’.

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