Scientific Machine Learning

Dr. Daigo Maruyama

Short Description

Students will have comprehensive machine learning techniques and the ability to formulate and solve complex probabilistic models by using the sum and product rules of probability. Through the machine learning techniques acquired in this course, in engineering design optimization students have ability to generate models which enable to explore the solutions actively and efficiently by exploiting the uncertainties obtained in the learning process. In addition, preprocessing such as feature extraction, which is often used in image recognition technology, can also be performed by the machine learning techniques acquired in this course. These contribute to the simplification of problems and cost efficiency in engineering problems in general, and also enable automatic sample generation, so to say, constructing new images in the above example. Furthermore, in scientific problems, it is used as a key technology to reveal essential physical quantities. Overall by viewing globally and unifiedly the theory of probability from Bayesian perspectives students enable to formulate probabilistic models actively and to acquire proper machine learning approaches on each problem setting. The course includes practical tutorials using computer programs. Recent applications of machine learning techniques to aerospace engineering are introduced as examples.

Prerequisites

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