2021 Applied programming and numerical analysis

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Academic unit or major
Undergraduate major in Transdisciplinary Science and Engineering
Instructor(s)
Yamashita Yukihiko  Akita Daisuke 
Course component(s)
Lecture / Exercise    (ZOOM)
Day/Period(Room No.)
Wed1-4(S321)  
Group
-
Course number
TSE.A324
Credits
2
Academic year
2021
Offered quarter
4Q
Syllabus updated
2021/10/11
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

The purposes of the first half of this class are to learn a basic programming technique of Python and to be able to make programs for numerical analysis by succeeding "Programming and numerical analysis". Students learn from basic grammar of Python to data structures such as list and array. And students learn algorithms of statistical processing and signal and image processing, and practice programmings.
The purpose of the second half of this class is to develop practical abilities such as applied numerical simulation methods, visualization of results by succeeding "Programming and numerical analysis". In the group work, students will experience not only numerical simulation or programming but also problem setting, modeling, and evaluation of results.

Student learning outcomes

Students can obtain the following abilities by this lecture
(1) Basic grammar of Python
(2) Algorithms of statistical processing and signal and image processing
(3) Basic programming

Keywords

Programming,numerical analysis, algorithm, Python, statistical processing, signal and image processing, modeling, visualization

Competencies that will be developed

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

Class flow

In the beginning part of each class, students learn about grammar of a programming language and algorithms. After that, make programs based on them.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Basic grammar and control of Python Be able to make simple programs of Python with branch and loops.
Class 2 Basic data structure and sort Be able to make programs of Python with list and array to sort them.
Class 3 Statistical processing Be able to make programs of Python to calculate average and variance from data and perform a statistical test.
Class 4 Signal and image processing Be able to make programs of Python for DFT and signal and image processing.
Class 5 Visualization of results and 3D modeling using Paraview, Newtonian flow simulation Be able to visualize results using Paraview
Class 6 Deep learning from scratch Be able to understand basic algorithm of deep learning
Class 7 Group work Prepare for the group presentation

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None

Reference books, course materials, etc.

John V. Guttag, "Introduction to Computation and Programming Using Python," MIT Press, 2013.

Assessment criteria and methods

Learn programming and be able to make programs by using algorithms of numerical analysis.
Practices and reports (100%)

Related courses

  • TSE.A307 : Programming and numerical analysis

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

None

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