2019 Workshop on Group Problem-Solving (ACLS)

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Yamamura Masayuki  Takinoue Masahiro 
Course component(s)
Lecture / Exercise
Day/Period(Room No.)
Intensive ()  
Group
-
Course number
ART.T453
Credits
2
Academic year
2019
Offered quarter
2Q
Syllabus updated
2019/3/19
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

Life science and computer science students will form small groups that solve practical problems in the field of computational life science.

Student learning outcomes

Life science students learn computer literacy for group work with computer science students. Computer science students learn basic mathematics for modeling and simulation in Computational life science field.

Keywords

Computational Life Science, Creative Collaborative Work

Competencies that will be developed

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

Class flow

In the first 8 classes, students are separated into life science students and computer science students, receiving instruction in separate classrooms. In each class the instructor lectures using original materials. In each class, through simple individual and group exercises, students' achievements are evaluated while also aiding their acquisition of knowledge. In the final 7 classes, students solve group problems in contest format in small groups of students with different majors.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction: Students will have their knowledge checked with questionnaires, which will determine group organization. mathematical basics, computer literacy, biological basics
Class 2 Bio-1 : networking and security CS-1 : overview of systems modeling with nonlinear differential equations network security, linear differential equations
Class 3 Bio-2 : Programming in Python 1 CS-2 : Non-linear systems 1 data types and operators, Laplace transfer, stability
Class 4 Bio-3 : Programming in Python 2 CS-3 : Non-linear systems 2 control flow, state representation
Class 5 Bio-4 : Programming in Python 3 CS-4 : Non-linear systems 3 commandline arguments, non-linear differential equations
Class 6 Bio-5 : Programming in Python 4 CS-4 : Non-linear systems 4 class and objects, phase diagram
Class 7 Bio-6 : Programming in Python 5 CS-6 : Non-linear systems 5 class definition, bifurcation
Class 8 Bio-7 : Presentation CS-7 : Topics Python program, systems modeling
Class 9 Introduction to creative collaborative work brain storming, KJ method
Class 10 group work 1 group work
Class 11 group work 2 group work
Class 12 group work 3 group work
Class 13 group work 4 group work
Class 14 group work 5 group work
Class 15 competition competition

Textbook(s)

Unspecified.

Reference books, course materials, etc.

For every class, instructors lecture independent topics with original handouts.

Assessment criteria and methods

Every class also includes simple exercises by individual students or by groups. These exercises help understanding the principle and also become materials for final evaluation. Ranking in the final competition will be added.

Related courses

  • none

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

Students must belong to the Education Academy of Computational Life Sciences Doctors Education Program (ACLS)

Other

none

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