Living systems adapts their environment flexibly. Functions of information processing in the living system have been modeled
in biomimetic computational systems. The biomimetic computational systems effectively solve problems which are difficult to
be modeled mathematically or have nonlinearity, with which classical computational methods has difficulty. Some of the biomimetic
computational systems have been established in engineering. In this course, students learn elementary techniques of artificial
neural network, which is gaining attention as artificial intelligence and deep learning in recent years, reinforcement learning,
genetic algorithms, their mathematical foundation and their application to practical problems. Additionally, the students
learn analyses for experimental data of biological information.
The purpose of this course is to learn biomimetic computational systems such as artificial neural networks, reinforcement
learning, genetic algorithms, their mathematical foundation, their application to practical problems, and analyses for experimental
data of biological information.
- Can understand mathematical foundation of biomimetic computational systems
- Can apply biomimetic computational systems to practical problems
- Can understand analyses for experimental data of biological information
| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Guidance - Biomimetic computational systems Basics of Machine learning 1 - Supervised learning |
assignments distributed in the class | 190minutes |
| 2. | Basics of Machine learning 2 - Regression analysis - Discriminant analysis |
assignments distributed in the class | 190minutes |
| 3. | Neural information processing 1 - Formal neuron - Perceptron |
assignments distributed in the class | 190minutes |
| 4. | Basics of Machine learning 3 - Optimization techniques |
assignments distributed in the class | 190minutes |
| 5. | Neural information processing 2 - Hierarchical neural networks and Backpropagation algorithm 1 |
assignments distributed in the class | 190minutes |
| 6. | Neural information processing 3 - Hierarchical neural networks and Backpropagation algorithm 2 |
assignments distributed in the class | 190minutes |
| 7. | Neural information processing 4 - Hierarchical neural networks and Backpropagation algorithm 3 |
assignments distributed in the class | 190minutes |
| 8. | Neural information processing 5 - Autoencoder - Convolutional neural networks |
assignments distributed in the class | 190minutes |
| 9. | Neural information processing 6 - Time series data - Recurrent neural networks |
assignments distributed in the class | 190minutes |
| 10. | Neural information processing 10 - dynamical systems and nonlinear dynamics, chaos dynamical systems - Hopfield type neural networks - Chaos dynamical systems |
assignments distributed in the class | 190minutes |
| 11. | Reinforcement learning - Basics of reinforcement learning - Application of reinforcement learning |
assignments distributed in the class | 190minutes |
| 12. | Genetics and information processing - Genetic algorithm - Mathematical models of population genetics |
assignments distributed in the class | 190minutes |
| 13. | Analyses for experimental data of biological information 1 - Statistical evaluation |
assignments distributed in the class | 190minutes |
| 14. | Analyses for experimental data of biological information 2 - Analysis of data in brain-computer interface, as an example - Application of biomimetic computational systems |
assignments distributed in the class | 190minutes |
| Total. | - | - | 2660minutes |
| assignments | Total. | |
|---|---|---|
| 1. | 45% | 45% |
| 2. | 40% | 40% |
| 3. | 15% | 15% |
| Total. | 100% | - |
Grade is judged by assignments given in each class (100%) .
The level of 60 points is to understand basics of mathematical foundation of biomimetic computational systems, their application to practical problems of and analyses for experimental data of biological information.
The level of 60 points is to understand basics of mathematical foundation of biomimetic computational systems, their application to practical problems of and analyses for experimental data of biological information.
Textbooks: Textbooks are specified in the class.
Materials will be distributed.
Materials will be distributed.
- Lunch break on Friday: Making appointment is recommended.
- Course that cultivates an ability for utilizing knowledge
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | N/A |
Last modified : Sun Mar 21 16:41:07 JST 2021