14717 items found.
This lecture is an introduction to Shannon's information theory. The essence of information theory is "what is information mathematically?". We will consider a mathematical model of "amount of information", "coding of information" etc and study their basic theory. In transmission and recording, we want to reduce the amount of data. On the other hand, in communication, there is a possibility of transmission error, and coding is required to minimize the transmission error. We will consider the basic idea and method for these problems.
Information theory is a basic theory of expression and transmission of "information". Typical applications include data compression, bit error detection / correction, and encryption. Information theory also plays an important role in machine learning algorithms.
The goal is to learn the quantitative treatment of "information" that is used in our daily life.
Computer graphics (CG) and mathematics have a strong connection. For instance, generating geometric shapes like curves and spheres, as well as performing shape operations such as translation, scaling, and rotation, are all reliant on mathematics. Especially in CG production methods that leverage mathematics, such as procedural graphics and algorithmic design, enhancing one's mathematical skills can significantly expand creative possibilities. Moreover, applying mathematics in CG can deepen one's visual understanding of mathematical concepts. In this lecture, we will explore the relationship between CG and mathematics from both perspectives.
Origami, an ancient Japanese game of folding paper to create shapes, has been the subject of research in a wide range of fields, including engineering, medicine, mathematics, art, and education, as a technique for creating shapes by folding thin materials or folding objects into smaller sizes. Currently, the Japanese-derived expression origami is widely used worldwide and is the subject of active international discussion. In this lecture, we will study not only traditional origami as represented by origami cranes but also origami as it relates to a wide range of scientific fields, from its geometric properties to engineering applications and its relationship to various problems in the field of mathematics. The lecture will also provide an outlook on the future of origami technology by explaining recent research presented at international conferences and other cutting-edge technology.
In addition, students will be encouraged to discover new ways to fold paper through various folding experiences throughout the lecture.
In this lecture, we will introduce various aspects of algorithms that form the basis of computer science and data science. No programming will be done.
Origami, an ancient Japanese game of folding paper to create shapes, has been the subject of research in a wide range of fields, including engineering, medicine, mathematics, art, and education, as a technique for creating shapes by folding thin materials or folding objects into smaller sizes. Currently, the Japanese-derived expression origami is widely used worldwide and is the subject of active international discussion. In this lecture, we will study not only traditional origami as represented by origami cranes but also origami as it relates to a wide range of scientific fields, from its geometric properties to engineering applications and its relationship to various problems in the field of mathematics. The lecture will also provide an outlook on the future of origami technology by explaining recent research presented at international conferences and other cutting-edge technology.
In addition, students will be encouraged to discover new ways to fold paper through various folding experiences throughout the lecture.
In this lecture, we will introduce various aspects of algorithms that form the basis of computer science and data science. No programming will be done.
In this lecture, we will learn about optimization problems. The optimization problem is to find a solution that minimizes (or maximizes) the objective function under certain constraints. This can appear in a wide range of situations, from assigning part-time shifts to matching residents and hospitals. In this lecture, we will cover topics related to database systems.
This lecture is an introduction to Shannon's information theory. The essence of information theory is "what is information mathematically?". We will consider a mathematical model of "amount of information", "coding of information" etc and study their basic theory. In transmission and recording, we want to reduce the amount of data. On the other hand, in communication, there is a possibility of transmission error, and coding is required to minimize the transmission error. We will consider the basic idea and method for these problems.
Information theory is a basic theory of expression and transmission of "information". Typical applications include data compression, bit error detection / correction, and encryption. Information theory also plays an important role in machine learning algorithms.
The goal is to learn the quantitative treatment of "information" that is used in our daily life.
These lecture series discuss the concept of mathematics and its application in research. We discuss probability theory and statistics. The lecture will be given interactively with lecturers and students, thus students' attendance is mandatory. Half of the class is to explain the concept of mathematical topics in the first half of the lecture, and its exercise is provided in the second half of the lecture.
DAD is a coined word combining the words Data&AI, Art, and Design. This class will provide students with the opportunity to learn how to understand these four elements [Data, AI, Art, Design] not separately but seamlessly by connecting them, knowing them as knowledge through concrete examples, embodying the knowledge through hands-on activities, and to acquire the foundation for applying mathematics in practical problems.
Origami, an ancient Japanese game of folding paper to create shapes, has been the subject of research in a wide range of fields, including engineering, medicine, mathematics, art, and education, as a technique for creating shapes by folding thin materials or folding objects into smaller sizes. Currently, the Japanese-derived expression origami is widely used worldwide and is the subject of active international discussion. In this lecture, we will study not only traditional origami as represented by origami cranes but also origami as it relates to a wide range of scientific fields, from its geometric properties to engineering applications and its relationship to various problems in the field of mathematics. The lecture will also provide an outlook on the future of origami technology by explaining recent research presented at international conferences and other cutting-edge technology.
In addition, students will be encouraged to discover new ways to fold paper through various folding experiences throughout the lecture.
In this lecture, we will introduce various aspects of algorithms that form the basis of computer science and data science. No programming will be done.
The objective of this course is to learn the advanced micro econometrics and pursue your own research topic by using the knowledge and skills that you acquired.
The objective of this course is to learn the advanced micro econometrics and pursue your own research topic by using the knowledge and skills that you acquired.
In modern times, many problems around us are mathematically abstracted and solved by using computers to perform calculations based on mathematical theory. Gain a better understanding of high school mathematics, linear algebra, and calculus by knowing how and how math was used to solve real problems.
This class focuses on the skills bioinformaticians use to explore and extract information from complex, large datasets. These data skills give you freedom; you’ll be able to look at any bioinformatics data (in any format, and files of any size) and begin exploring data to extract biological meaning.
Throughout the class, I will emphasize working in a robust and reproducible manner. Reproducibility means that your work can be repeated by other researchers and they can arrive at the same results. For this to be the case, your work must be well documented, and your methods, code, and data all need to be available so that other researchers have the materials to reproduce everything. If a workflow run on a different machine yields a different outcome, it is neither robust nor fully reproducible. These are themes that reappear throughout the class.
This class focuses primarily on handling tabular plain-text data formats. Tabular data is terrific for honing your data skills. Even if your goal is to analyze other types of data in the future, tabular data serves as great example data to learn with. Developing the text-processing skills necessary to work with tabular data will be applicable to working with many other data types. Thus, this class will teach you useful computational tools and data skills that will be helpful in your research.
Researchers from all disciplines will use Bioinformatics Data Skills to tackle problems in their fields (e.g., biology, language, music, socio-economic factors contributing to the COVID-19 pandemic, etc.).
In this lesson, we learn "sports analytics" from a multifaceted perspective.
It analyzes the data in the classes. And ultimately make up material creation to seek a settlement to the approval person.Not only the technical skills of data analysis , learn the importance of objective setting and explanatory variables
Data mining has gained interest among business practitioners in a variety of fields. Almost every organization collects data, which
can be analyzed to support making better decisions and improving policies.
Electronic data capture has become inexpensive and ubiquitous due to innovations such as the internet, e-commerce, point-of-sale devices. As a result, data mining is a rapidly growing field concerned with developing techniques to assist managers in making intelligent use of these repositories. The area of data mining has evolved from the disciplines of statistics and artificial intelligence.
This course will examine data mining methods and provide an opportunity for hands-on exercises with algorithms for data mining using R-language software and cases.
In this lecture we overview how mathematical science is useful to analyze real problems.The point is, not to memorize formulas and results, to understand the process how to formulate real problem to mathematical problem.
In this lecture we overview how mathematical science is useful to analyze real problems.The point is, not to memorize formulas and results, to understand the process how to formulate real problem to mathematical problem.
"Sports analytics" is becoming more and more important every year in the global sports industry. It's used for team strategies, scouting players, marketing, sports betting, and much more. The amount of sports data increases every year for instance, GPS, health information, customer data, and data by sports data providers. The aim of this course is learning basic statistics and machine learning using the R language with actual sports data.
Researchers from all disciplines, including Policy Management and Environment and Information Studies, will apply sequence analysis methods to tackle problems in their fields (biology, language, manuscript, music, etc.).
Sequence analysis is a broad field, covering any kinds of analyses of textual sequences; e.g. those representing genomes (DNA) and proteins (amino acids). The biological sequence analyses include predicting gene function, inferring phylogenetic relationships, and ancestral reconstruction (Coghlan, 2017; Hall, 2017). For instance, phylogenetic trees inferred from viral sequence data can be used to estimate viral emergence, characterize the geographic spread of the virus, and identify instances of adaptive mutations (Martin et al., 2021). The sequence analysis methods have been used not only in the field of biology, but also in genealogy of manuscripts (Barbrook et al., 1998) and quantitative evaluation of melodic similarity (Savage et al., 2018). Thus, text-processing skills necessary to analyze sequence data can be applied to the analysis of data in other fields.
This course will provide the introduction to the main tools and databases used in the analysis of sequence data and explains how these can be used together to answer biological questions.
Sequence analysis is a broad field, covering any kinds of analyses of textual sequences; e.g. those representing genomes (DNA) and proteins (amino acids). The biological sequence analyses include determining genome structures, identifying protein-coding regions (genes), predicting gene function, inferring phylogenetic relationships, and ancestral reconstruction (Coghlan, 2011; Hall, 2017). Recent studies showed that genomics and phylogenetics can track spread and evolution of novel coronavirus ([https://nextstrain.org/]). The sequence analysis methods have been used not only in the field of biology, but also in genealogy of manuscripts (Barbrook et al., 1998) and quantitative evaluation of melodic similarity (Savage et al., 2018). Thus, text-processing skills necessary to analyze sequence data can be applied to the analysis of data in other fields.
This course will provide the introduction to the main tools and databases used in the analysis of sequence data and explains how these can be used together to answer biological questions. Examples of analysis include retrieving DNA and protein sequences from public databases, DNA sequence statistics (length, GC content, DNA words, and local variation in base composition), pairwise sequence alignment (dotplot, global sequence alignment, and local sequence alignment), multiple sequence alignment, and phylogenetic inference, etc.
Students from all disciplines will use the sequence analysis methods to tackle problems in their fields (biology, language, manuscript, music, etc.).
Data mining has gained interest among business practitioners in a variety of fields. Almost every organization collects data, which
can be analyzed to support making better decisions and improving policies.
Electronic data capture has become inexpensive and ubiquitous due to innovations such as the internet, e-commerce, point-of-sale devices. As a result, data mining is a rapidly growing field concerned with developing techniques to assist managers in making intelligent use of these repositories. The area of data mining has evolved from the disciplines of statistics and artificial intelligence.
This course will examine data mining methods and provide an opportunity for hands-on exercises with algorithms for data mining using R-language software and cases.
In this lecture we overview how mathematical science is useful to analyze real problems.The point is, not to memorize formulas and results, to understand the process how to formulate real problem to mathematical problem.