[Japanese-medium]Artificial and Natural Intelligence Course

Learning from brain functions to realize intelligent systems as artificial intelligence

Learning the organic relation between natural intelligence and artificial intelligence

Students will systematically learn comprehensive knowledge and skills for data analysis, mathematical modeling and simulation, based on the data obtained by measurement of natural and social phenomena. Achievements enable students to understand the target systems through obtaining real data and extracting information, and to realize intelligent systems by information technology.

What students can learn01
Students can understand the brain functions through measurement

Students will learn physiological and anatomical knowledge of the brain, as the basis for human intelligence information systems, and understand the brain functions through measurement by conducting sensory and perception experiments. In addition, students will learn theoretical frameworks for comprehending the mechanisms of brain functions from an information processing perspective and enhance their understanding of engineering applications.

Physiological Engineering
Students will learn physiological and anatomical knowledge of the sensory system and acquire the measurement analysis methods.
Color Science
Students will learn methods and principles for handling color, which is a psychophysical quantity, from a quantitative and engineering perspective.
Students will learn the basics of psychophysics, which is a system for studying perception and sense.
Brain Function in Data Processing
Students will learn about the structure and function of the brain, which is a representative example of an intelligent information processing system.

What students can learn02
Students can realize intelligent systems by information technology

In order to make computers learn as the human naturally does by interacting with the environment, we need to understand learning algorithms. Students will acquire basic knowledge of underlying technologies for machine learning and pattern recognition, and deepen their understanding through target problems and case studies.

Machine Learning
Students will learn the basic techniques indispensable for statistical machine learning and statistical pattern recognition.
Kansei Engineering
Students will learn basic and applicable skills for handling human sensitivity (kansei) in engineering, such as methods for evaluating sensitivity and building design models.

What students can learn03
Students can model and simulate the real world

Students will learn methods to model the basic underlying principles in various natural and social phenomena in the real world, and learn mathematical and algorithmic frameworks for the optimization. Students will study how to formalize and model the target problems for computer simulation, solve the problem by and on computers, and deepen their understanding through programming exercises.

Simulation Engineering
Students will learn about computer simulation, which is a problem-solving method using computers.
Mathematical Optimization
Students will learn how to model real-world targets and use computers to perform optimization.