14717 items found.
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Baye's theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Baye's theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Baye's theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
This is an introduction to the mathematical theory of probability. We begin with basics of set theory, mathematical logic and combinatorics, based on which we develop probability theory. After introducing the concept of probability, we cover basic topics of probability theory: conditional probability, independency, Bayes' theorem, random variables, probability distributions, expectation, variation, central limit theorem.
This is an introduction to the mathematical theory of probability. We begin with basics of set theory, mathematical logic and combinatorics, based on which we develop probability theory. After introducing the concept of probability, we cover basic topics of probability theory: conditional probability, independency, Bayes' theorem, random variables, probability distributions, expectation, variation, central limit theorem.
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Bayes' theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Bayes' theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
This class is an introduction to probability. Probability is a theory that quantifies uncertain phenomena and is the fundamental mathematics in a wide range of fields such as data science, economics, and engineering. After covering some basics of probability (joint probability, conditional probability, Baye's theorem etc), we will learn quantitate aspects of probability distributions (random variable, expectation values, variance etc).
Probability and statics are well established branches of mathematics that has applications in all areas of technology today. This course mainly presents a solid foundation for probability and the introduction of statics, explaining its ideas and techniques necessary for a firm understanding of the topic.
Probability and statics are well established branches of mathematics that has applications in all areas of technology today. This course mainly presents a solid foundation for probability and the introduction of statics, explaining its ideas and techniques necessary for a firm understanding of the topic.
Probability and statics are well established branches of mathematics that has applications in all areas of technology today. This course mainly presents a solid foundation for probability and the introduction of statics, explaining its ideas and techniques necessary for a firm understanding of the topic.
We will learn time series econometric techniques. In particular, we will study ARMA models, including parameter estimation and forecast. Then, we will study VAR models that deal with more than one series. We will further study state-space models, unit-root tests, and co-integration.
We study Complex Analysis. We understand some properties of complex functions
such as Cauchy's theorem, Cauchy's formula, Residues theorem, etc, which are
completely different form real functions. We don't touch the proofs, but we
understand what theorems imply, and master some calculations.
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.
We study Complex Analysis. We understand some properties of complex functions
such as Cauchy's theorem, Cauchy's formula, Residues theorem, etc, which are
completely different form real functions. We don't touch the proofs, but we
understand what theorems imply, and master some calculations.
In recent years, Bayesian statistics has gained prominence in various fields such as economics, finance, medicine, psychology, and marketing. This lecture will begin by covering the basics of probability theory and then explore key aspects of Bayesian statistics, including Bayes' theorem, Bayesian inference, numerical analysis using Markov chain Monte Carlo methods, and Bayesian statistical modeling. The session will also incorporate practical exercises using Python.
This course is designed to provide an introduction to understanding, evaluating data, and making rational data-driven decisions.
Using data is a powerful tool for conducting research. However, several points must be considered to draw correct conclusions from the data. For example, data rarely give an accurate picture of the research subject as it is. Even in carefully planned experiments and surveys, it is inevitable that there will be a variety of errors in the measurements. In the case of statistical surveys, where we are trying to obtain knowledge about the whole (population) from a subset of data (sample), the problem is further complicated by adding sampling error. We must deal with data affected by such chance variation and find conclusions in the face of uncertainty.
Statistics is a way to manage risk under uncertainty and to draw reasonable conclusions from data. In this course, you will learn "Estimation" (point estimation and interval estimation) and "Test" (tests of association in contingency tables, analysis of variance, and tests of differences in means of two groups), which are methods for making surrogate inferences about a population from a sample. The goal is to acquire the ability to understand the mechanics of each analysis, perform them appropriately, and write reports.
The course is characterized by its focus on mastering concepts and interpreting data and statistical analysis results rather than on detailed computational techniques and by its practice of statistical learning in the computer age, including computer-based experiments to deepen students' understanding of theoretical distributions.
We study Complex Analysis. We understand some properties of complex functions
such as Cauchy's theorem, Cauchy's formula, Residues theorem, etc, which are
completely different form real functions. We don't touch the proofs, but we
understand what theorems imply, and master some calculations.
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.
In recent years, the Bayesian approach has been attracting attention not only in the natural sciences, such as biostatistics and spatial statistics, but also in the social sciences, such as marketing, policy analysis, and econometrics. In this class, we will cover the basics and applications of Bayesian statistics, assuming a basic knowledge of classical statistics, and will include exercises in R and other languages. Markov chain Monte Carlo, empirical Bayes and hierarchical Bayes, Bayesian inference on regression and correlation, Bayesian econometrics, etc.
This class aims to study about statistical modeling such as linear regression model, general linear regression model and general linear mixture model.
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 course is designed to be an introduction to understanding and evaluating data and making rational decisions based on that data. This year, the focus is on multivariate analysis techniques. What you will learn is the representation and summary statistics of quantitative and qualitative data, correlations and principal components, factor analysis, and analysis of covariance structures.
The focus is on mastering concepts and interpreting the results of data and statistical analysis, rather than detailed computational techniques.
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.
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 linear programming problems, nonlinear programming problems, and integer programming problems.