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# Application of Biometrics and Biostatistics to Agricultural Science

Application of Biometrics and Biostatistics to Agricultural Science
Statistics and biometrics have become one of the most important disciplines in various fields, not only in agricultural sciences. This is mainly due to the following three factors: (1) Improvements in data measurement techniques have led to the collection of diverse and large amounts of biological and agronomical data that were not collected in the past, (2) Data science methods have been developed to connect and model the collected biological and agronomical data, (3) Computational power to use such methods has improved. These circumstances have made statistical and biometric methods essential tools for extracting knowledge from diverse and voluminous biological and agronomical data.
In this lecture, various types of biological and agronomic data will be used as examples to illustrate the methods used to analyze them. The lecture will be given in a hands-on style using R, Python and Matlab, and is intended to provide students with practical analysis techniques. The first one-third of the entire course is devoted primarily to analytical methods related to summarizing, visualizing, and modeling relationships in multivariate data. In the second one-third of the course, students will learn about linear models, linear mixed models, local regression, and nonlinear models. In the last one-third of the course, students will learn about image analysis, machine learning, and deep learning. Although a wide range of methods from introductory to advanced will be used, emphasis will be placed on acquiring the ability to perform actual analysis on one's own, rather than on explaining the principles of the methods.

コース名

3901165
Application of Biometrics and Biostatistics to Agricultural Science

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11th April: Guidance and introduction 18th April: Understanding variability in multivariates: dimensional reduction and visualization 25th April: Predicting univariate based on multivariates: multivariate regression, regularized regression, kernel regression 2nd May: Predicting classes based on multivariates: logistic regression, support vector machines, random forests 9th May: Probability distribution other than Gaussian distribution; Maximum likelihood; Generalized Linear Model and its applications 16th May: Fixed effect; Random effect; Generalized Linear Mixed Model and its applications 23rd May: Local regression; Spline; Generalized Additive Model and its applications 6th June: Non-linear functions in agriculture; Non-linear regression; introduction to MCMC 13th June: Fundamentals of image analysis: Vectors and matrices operation 20th June: Image analysis for agriculture: Machine learning 27th June: Image analysis for agriculture: Deep learning 4th July: Image analysis for agriculture: Multi-dimensional imaging 11th July: Modeling relationships within multivariates: canonical correlation Analysis, PLS regression, network analysis 18th July: Final Q&A and Wrap Up

There will be no specific textbook, and lectures will be given on the basis of resumes distributed each time. To consolidate the basic knowledge learned in class, twelve small reports will be assigned.

Evaluation will be based on reports. However, no grade will be given for poor attendance.

None in particular.

None in particular.

In this course, students will learn practical statistical and data science methods using R, Python, and Matlab. Students are required to use these programming environments on their own computers. The installation of these programming environments will be explained in the first class.
その他
Lectures are conducted in an online format (fully online). Language used: English (Medium of instrucion: E、Course materials: E) Lecturers: Hiroyoshi Iwata (https://www.researchgate.net/*****) Gen Sakurai (https://www.researchgate.net/*****) Wei Guo (https://www.researchgate.net/*****) Lecturers writing this syllabus: Hiroyoshi Iwata