Information Engineering on Living Body

Course title

F0840600

Click to show questionnaire result at 2019

Course description

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.

Purpose of class

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.

Goals and objectives

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

Language

Japanese

Class schedule

	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.	Basics of Machine learning 3 - Optimization techniques	assignments distributed in the class	190minutes
4.	Neural information processing 1 - Formal neuron - Perceptron	assignments distributed in the class	190minutes
5.	Neural information processing 2 - Hierarchical neural networks - Backpropagation algorithm	assignments distributed in the class	190minutes
6.	Neural information processing 3 - Time series data - Recurrent neural networks	assignments distributed in the class	190minutes
7.	Neural information processing 4 - dynamical systems and nonlinear dynamics - Hopfield type neural networks	assignments distributed in the class	190minutes
8.	Neural information processing 5 - Chaos dynamical systems - Chaotic neural networks	assignments distributed in the class	190minutes
9.	Neural information processing 6 - Boltzmann Machine	assignments distributed in the class	190minutes
10.	Reinforcement learning - Basics of reinforcement learning - Application of reinforcement learning	assignments distributed in the class	190minutes
11.	Genetics and information processing - Genetic algorithm - Mathematical models of population genetics	assignments distributed in the class	190minutes
12.	Analyses for experimental data of biological information 1 - Statistical evaluation	assignments distributed in the class	190minutes
13.	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
14.	Examination and review -Examination coverage: all topics	Summarize all topics	190minutes
Total.	-	-	2660minutes

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	four assignments	examination	Total.
1.	25%	15%	40%
2.	25%	15%	40%
3.	10%	10%	20%
Total.	60%	40%	-

Evaluation method and criteria

-Grade is judged by four assignments (60%) and the result of examination (40%).
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 and reference materials

Presentation materials in each class will be uploaded online.
Reference books (in Japanese)
瀧雅人，”機械学習スタートアップシリーズこれならわかる深層学習入門 (KS情報科学専門書)”，講談社

Prerequisites

Calculus, linear algebra

Office hours and How to contact professors for questions

Lunch break on Friday: Making appointment is recommended.

Relation to the environment

Non-environment-related course

Regionally-oriented

Non-regionally-oriented course

Development of social and professional independence

Course that cultivates an ability for utilizing knowledge

Active-learning course

More than one class is interactive

Course by professor with work experience

Work experience	Work experience and relevance to the course content if applicatable
N/A	N/A

Last modified : Sun Mar 21 14:53:56 JST 2021