2016 Fundamentals of System Science

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Academic unit or major
Undergraduate major in Systems and Control Engineering
Instructor(s)
Hasegawa Osamu 
Course component(s)
Lecture
Mode of instruction
 
Day/Period(Room No.)
Tue7-8(S515)  Fri7-8(S515)  
Group
-
Course number
SCE.S302
Credits
2
Academic year
2016
Offered quarter
3Q
Syllabus updated
2016/4/27
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

This course provides students with a wide range of skills of basic mathematical science that is necessary to system design and control.

Student learning outcomes

At the end of this course, students will be able to understand the basics of various mechanisms behind systems,
And furthermore, lay the groundwork for the expertize acquisition.

Keywords

Machine Learning, Artificial Intelligence, Complex Systems, Intelligent Systems, Intelligent Robots

Competencies that will be developed

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

Class flow

Dr Hasegawa will give lectures in the first half of the academic year, after that, some supervisors who are conducting advanced research in the field of computational intelligence and systems science will give lectures about systems and mathematical science form diverse perspectives.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Learner and learning methods Write reports about learning machines.
Class 2 Basics of the Bayes theory Write reports about bayes theory.
Class 3 Probabilistic models and discriminant functions Write reports about mathematical models.
Class 4 Basics of neural networks Write reports about neural networks.
Class 5 Time series data analysis Data analysis/analyses, practice
Class 6 Image data analysis Image processing, practice
Class 7 Multi-modal data analysis Write reports about intelligent robots.
Class 8 Agent systems Homework specified by the instructor.
Class 9 Knowledge systems Homework specified by the instructor.
Class 10 Complex networks Homework specified by the instructor.
Class 11 Intelligent interactive systems Homework specified by the instructor.
Class 12 Self organizing systems Homework specified by the instructor.
Class 13 Evolutionary systems Homework specified by the instructor.
Class 14 System biology Homework specified by the instructor.
Class 15 Molecular robotics Homework specified by the instructor.

Textbook(s)

Text book specified by the instructor.

Reference books, course materials, etc.

All materials used in class can be found on OCW-i.

Assessment criteria and methods

Students' course scores are based on homework and final exams.

Related courses

  • ZUS.I301 : Introduction to Artificial Intelligence
  • ICT.H318 : Foundations of Artificial Intelligence (ICT)
  • CSC.T272 : Artificial Intelligence
  • ART.T548 : Advanced Artificial Intelligence

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

No prerequisites.

Other

Lectures in second-half are subject to variation.

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