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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Information and Communications Engineering
- Instructor(s)
- Kumazawa Itsuo
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Tue1-2(G224) Fri1-2(G224)
- Group
- -
- Course number
- ICT.H416
- Credits
- 2
- Academic year
- 2018
- Offered quarter
- 3Q
- Syllabus updated
- 2018/9/17
- Lecture notes updated
- 2018/9/16
- Language used
- Japanese
- Access Index
-
Lecture
Lecture 1 Introduction of biological neural network (Neurons and Neural Networks).Artifical models of neuron (Derministic and probablistic models. Binary and continuous models.
2018.9.28(Fri.) 1-2Session
Lecture
Lecture 2 Artifical models of neural network (Recurrent and Feed forward models). Introduction of statistical mechanics (Magnetic Systems and Spin Glass Models
2018.10.2(Tue.) 1-2Session
Lecture
Lecture 3 How to understand the behavior of highly parallel system like a brain. Analogy between Neural Networks and Spin Glass Models. Enegy minimization by the deterministic models of recurrent neural network
2018.10.9(Tue.) 1-2Session
Lecture
Lecture 4 Analysis of Neural Computaion by Boltsmann's Theory.
2018.10.12(Fri.) 1-2Session
Lecture
Lecture 5
2018.10.16(Tue.) 1-2Session
Lecture
Lecture 6
2018.10.19(Fri.) 1-2Session
Lecture
Lecture 7 Computer simulation of deterministic and probablistic models of recurrent neural network.
2018.10.23(Tue.) 1-2Session
Lecture
Lecture 8 Application of a recurrent neural network for solving simultaneous equations and Four Queen Problem.
2018.10.26(Fri.) 1-2Session
Lecture
Lecture 9 Tips for efficial computation: Ergodicity and automatic determination of connection weight.
2018.10.30(Tue.) 1-2Session
Lecture
Lecture 10 Programing and application of the recurrent neural network Part 1: Eight Queen Problem。
2018.11.2(Fri.) 1-2Session
Lecture
Lecture 11
2018.11.6(Tue.) 1-2Session
Lecture
Lecture 12
2018.11.9(Fri.) 1-2Session
Lecture
Lecture 13 Programing and application of the recurrent neural network Part 2: Travelling Salesman Problem.
2018.11.13(Tue.) 1-2Session
Lecture
Lecture 14 Learning of the recurrent neural network Part 1: Mathematical techniques.Learning of the recurrent neural network Part 2: Learning algorithm for Boltzmann Machine.
2018.11.16(Fri.) 1-2Session
Lecture
Lecture 15 Computer Simulation of a leaning algorithm of recurrent neural network.
2018.11.20(Tue.) 1-2Session
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