2774 items found.
This class is an introduction to calculus. Differential and integral calculus is a theory for analyzing changes and accumulations of targets, respectively, and has many applications in data science, economics, science and engineering, etc. In fact, calculus and linear algebra are considered as the most important mathematics at universities. In this class, we will learn not only calculus of one variable functions but also polynomial approximation of one variable functions and calculus of multivariate functions.
This class is an introduction to calculus. Differential and integral calculus is a theory for analyzing changes and accumulations of targets, respectively, and has many applications in data science, economics, science and engineering, etc. In fact, calculus and linear algebra are considered as the most important mathematics at universities. In this class, we will learn not only calculus of one variable functions but also polynomial approximation of one variable functions and calculus of multivariate functions.
行列とベクトルを学習します。連立一次方程式の解法、行列式、逆行列など行列やベクトルに関するいろいろな計算を習得すると共に、線形空間とその間の線形写像という抽象的な概念を理解します。行列は一次変換とみなされ、その固有値と固有ベクトル、行列の対角化はそのは一次変換を特徴付けます。統計学を含む多くの分野で現われる概念です。
This class is an introduction to linear algebra. Linear algebra is a theory about vectors and matrices, and has many applications in data science, economics, engineering etc. In fact, linear algebra and calculus are considered as the most important mathematics at universities. In this class, we will learn the basic ideas of linear algebra from both algebraic and geometric point of views.
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.
The aim of this course is to introduce the basics of medical and health data science, data collection, data management, data analysis and biostatistics to better comprehend medical literature and publications. By the end of the course, skills and methodology for basic statistical analysis needed for medical publication will be acquired.
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.).
In this lesson, we learn "sports analytics" from a multifaceted perspective.
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.
Based on “Introduction of Statistics,” this course will enhance student’s understanding of the theories and practices of data science and develop the following statistical abilities: discovering the problems of the current status, hypothesizing and building the models based on data, and verifying them. It will focus on applicative topics of linear models (model selection, logistic regression, and generalized linear model etc.) and the various methods of multivariate analyses such as principal component analysis, discriminant analysis, variance analysis, factor analysis, cluster analysis, and tree-model.
This class aims to study about statistical modeling such as linear regression model, general linear regression model and general linear mixture model.
In recent years, Bayesian statistics has been attracting attention in various fields such as economics, finance, medicine, psychology, and marketing.
In this lecture, starting from the basics of classical statistics, I will explain Bayes' theorem, Bayesian inference, Markov chain Monte Carlo method, model selection in Bayesian statistics, applications of Bayesian estimation to normal distribution models and regression analysis models, and hierarchical Bayesian models to handle individual differences. The lecture will be given in Python.
Exercises using Python will be included in the lecture.
This class discusses how several phenomena could be formulated in mathematical modeling. Each lecture introduces one phenomenon and a mathematical model that describes the phenomenon. This series of lectures firstly addresses modeling with differential equations, and in the later part, mathematical analysis of perceptual phenomena in human psychology are also discussed.
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.
The aim of this course is to provide knowledge of experiments for life science. In the class, students study the basic skills for experiments of DNA and protein.
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 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.
Provide lectures and exercises on various phenomenon in Earth and Planetary Science, which consists of space/planet, atmosphere/ocean, earthquake/volcano, rock/mineral, and geological earth history.
Since many phenomenon in Earth and Planetary Science are governed by equations, deep understanding can be obtained if one knows how to solve the equations. In classes, we focus on a particular phenomena and students will have a set of lecture and exercise on the phenomena.
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.
Artificial intelligence may have a great impact on society in the future. In order to understand capabilities and limits of artificial intelligence, it is necessary to understand computers as its foundation.
In the first half of this course, we learn fundamental knowledge of practical usage of computers and networks in SFC. In the second half, we learn programming skills which are necessary to take advantage of computers.
After this course, you will be able to learn advanced programming skills in Fundamentals of Information Technology 2.
Artificial intelligence may have a great impact on society in the future. In order to understand capabilities and limits of artificial intelligence, it is necessary to understand computers as its foundation.
In the first half of this course, we learn fundamental knowledge of practical usage of computers and networks in SFC. In the second half, we learn programming skills which are necessary to take advantage of computers.
After this course, you will be able to learn advanced programming skills in Fundamentals of Information Technology 2.
Artificial intelligence may have a great impact on society in the future. In order to understand capabilities and limits of artificial intelligence, it is necessary to understand computers as its foundation.
In the first half of this course, we learn fundamental knowledge of practical usage of computers and networks in SFC. In the second half, we learn programming skills which are necessary to take advantage of computers.
After this course, you will be able to learn advanced programming skills in Fundamentals of Information Technology 2.
Artificial intelligence may have a great impact on society in the future. In order to understand capabilities and limits of artificial intelligence, it is necessary to understand computers as its foundation.
In the first half of this course, we learn fundamental knowledge of practical usage of computers and networks in SFC. In the second half, we learn programming skills which are necessary to take advantage of computers.
After this course, you will be able to learn advanced programming skills in Fundamentals of Information Technology 2.
In Fundamentals of Information Technology 2, the goal is to take what you learned in Fundamentals of Information Technology 1 further and create a program of a certain size by yourself.
When creating a program, it is rare that you create everything yourself from nothing at all, and usually you create it using ready-made parts called libraries. There are many types of libraries depending on what you want to make, but this time we will use a library called Pyxel for making retro 2D games.
At first, we will use Pyxel to review Fundamentals of Information Technology 1 and study Python features that were not covered in Fundamentals of Information Technology 1. After that, we will make our own original game.