Our Courses

Discrete Mathematics

Discrete Mathematics

Discrete mathematics forms the mathematical foundation of computer and information science. It is also a fascinating subject in itself.
Learners will become familiar with a broad range of mathematical objects like sets, functions, relations, graphs, that are omnipresent in computer science. Perhaps more importantly, they will reach a certain level of mathematical maturity - being able to understand formal statements and their proofs; coming up with rigorous proofs themselves; and coming up with interesting results.

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  • 42 hours
  • English
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Integral Calculus and Numerical Analysis for Data Science

Integral Calculus and Numerical Analysis for Data Science

Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will provide an intuitive understanding of foundational integral calculus, including integration by parts, area under a curve, and integral computation. It will also cover root-finding methods, matrix decomposition, and partial derivatives.
This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program.

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  • 4 hours
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Advanced Linear Models for Data Science 2: Statistical Linear Models

Advanced Linear Models for Data Science 2: Statistical Linear Models

Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
- A basic understanding of linear algebra and multivariate calculus.
- A basic understanding of statistics and regression models.
- At least a little familiarity with proof based mathematics.
- Basic knowledge of the R programming language.

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  • 6 hours
  • English
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Stability and Capability in Quality Improvement

Stability and Capability in Quality Improvement

In this course, you will learn to analyze data in terms of process stability and statistical control and why having a stable process is imperative prior to perform statistical hypothesis testing. You will create statistical process control charts for both continuous and discrete data using R software. You will analyze data sets for statistical control using control rules based on probability. Additionally, you will learn how to assess a process with respect to how capable it is of meeting specifications, either internal or external, and make decisions about process improvement.

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  • 10 hours
  • English
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Precalculus: Relations and Functions

Precalculus: Relations and Functions

This course helps to build the foundational material to use mathematics as a tool to model, understand, and interpret the world around us. This is done through studying functions, their properties, and applications to data analysis. Concepts of precalculus provide the set of tools for the beginning student to begin their scientific career, preparing them for future science and calculus courses. This course is designed for all students, not just those interested in further mathematics courses.

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  • 12 hours
  • English
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Bayesian Statistics: Time Series Analysis

Bayesian Statistics: Time Series Analysis

This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models.

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  • 22 hours
  • English
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Logic for Economists

Logic for Economists

This course provides a very brief introduction to basic mathematical concepts like propositional and predicate logic, set theory, the number system, and proof techniques. At the end of the course, students will be able to
(1) detect the logical structure behind simple puzzles
(2) be able to manipulate logical expressions
(3) explain the connection between logic and set theory
(4) explain the differences between natural, integer, rational, real and complex numbers
(5) recognise different basic proof techniques

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  • 7 hours
  • English
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Regression Modeling in Practice

Regression Modeling in Practice

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results.

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  • 11 hours
  • English
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Regression and Classification

Regression and Classification

Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!

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  • 35 hours
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Geometría Analítica Preuniversitaria

Geometría Analítica Preuniversitaria

Líneas rectas, círculos, parábolas, elipses e hipérbolas son figuras geométricas que encontramos en nuestro derredor. Por ejemplo, mucha gente sabe que los planetas en nuestro sistema solar se mueven en órbitas elípticas teniendo al astro rey en un foco de esta figura. Sin embargo, pocos saben que la plaza de San Pedro en el Vaticano está construída sobre elipses donde sus focos se encuentran sobre las fuentes donde mucha gente se toma fotos. Estos son dos ejemplos que muestran la importancia de las figuras geométricas en nuestra vida.

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Detección de objetos

Detección de objetos

¿Te interesa la visión por computador? ¿Te gustaría conocer qué métodos puedes utilizar para detectar y reconocer objetos en una imagen?

En este curso te introducirás en los principios básicos de cualquier sistema automático de detección y reconocimiento de objetos en imágenes. A lo largo del curso analizaremos diferentes métodos de representación y clasificación que te permitirán abordar casos de aplicación de complejidad creciente.

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Clasificación de imágenes: ¿cómo reconocer el contenido de una imagen?

Clasificación de imágenes: ¿cómo reconocer el contenido de una imagen?

¿Te interesa la visión por computador? ¿Te gustaría saber cómo se puede reconocer el contenido visual de las imágenes y clasificarlas a partir de su contenido?

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Fundamentos de estadística aplicada

Fundamentos de estadística aplicada

El curso está orientado a profesionales de diferentes campos, que estén interesados en adquirir conceptos fundamentales de estadística aplicada. El contenido del curso será particularmente útil para profesionales que estén interesados en adelantar estudios de postgrado en ingeniería, administración o economía, entre otras profesiones, y que requieran de una adecuada fundamentación en estadística. También será de utilidad para estudiantes universitarios que deseen reforzar o complementar su formación básica en estadística, aprovechando los diferentes recursos con los que cuenta este MOOC.

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程序开发项目实践

程序开发项目实践

作为“程序设计与算法”系列专项课程的结业实践项目,我们联合腾讯公司,为大家设计了一个实际应用问题——搜索引擎设计。这是互联网公司中极具代表的实际开发项目。如何高效地进行检索?如何有效地提升搜索的精度?… 将要求你解决一系列搜索引擎设计中面临的实际问题。通过这个项目实践,我们希望能够对你的实际编程能力进行衡量,也希望你能够充分展现自己所学到的知识和技能。我们将在项目展开的过程中提供腾讯资深工程师的专业解答,并从完成项目的学习者中选拔部分同学参与在腾讯公司的现场交流。完成本实践项目,表明你已经具备了在企业承担程序研发工作的能力。

第一期的结业实践项目初步定为2016年6月。

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Comportamiento adaptativo

Comportamiento adaptativo

Los seres vivos han evolucionado en entornos cambiantes, por lo que han desarrollado mecanismos que les permiten exhibir comportamiento adaptativo. Usando el método sintético, podemos construir sistemas artificiales adaptativos que implementen dichos mecanismos, con lo cual también podemos incrementar nuestra comprensión de los sistemas naturales.

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  • 9 hours
  • Spanish
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Funciones algebraicas y trascendentes

Funciones algebraicas y trascendentes

En este curso estudiarás las funciones algebraicas y trascendentes desde su definición y notación.
Resolverás problemas de la vida cotidiana que se modelan a través de funciones:
Polinomiales
Racionales
Con Radicales
Exponenciales
Logarítmicas
Trigonométricas
Para las cuales utilizarás conceptos y procedimientos de aritmética, álgebra y trigonometría, así como de la geometría euclidiana y de la analítica.

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  • 72 hours
  • Spanish
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Procesamiento de Imágenes

Procesamiento de Imágenes

En este curso se estudiarán los fundamentos teóricos básicos que son aplicados en el área de Procesamiento de Imágenes, tales como formación de imágenes, mejoramiento de las imágenes en el dominio del espacio y de la frecuencia, filtros digitales, restauración de imágenes, procesamiento morfológico y segmentación tanto en imágenes en blanco y negro como a color. Los fundamentos teóricos aprendidos serán aplicados a problemas prácticos probando y programando algoritmos de procesamiento de imágenes en Python.

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高级数据结构与算法

高级数据结构与算法

学习了基本的数据结构后,我们已经可以用程序来解决现实中的一些问题了。但是,怎样提升程序在运行效率呢?
\t如何快速地把图书按序号从小到大整理好?如何通过一个ID编号在数据库中高效地查找相对应的信息?如何迅速找到所有内容中含有“数据结构”的文档?《高级数据结构与算法》将通过使用高级的数据结构和高效的算法,让你学会如何解决这些对运行时间要求比较严格的问题。
\t高级数据结构和算法能够根据实际情况,满足一些复杂问题对数据规模、运行时间的要求,帮助我们更有效地解决问题。当我们面对实际问题的时候,高级数据结构和算法让我们有更广泛的空间,选择出与问题本身最为契合的数据结构,并利用相关算法来提升运行效率。
\t完成这门课之时,你将掌握多维数组、广义表、Trie树、AVL树、伸展树等高级数据结构,并结合内排序、外排序、检索、索引有关的算法,高效地解决现实生活中一些比较复杂的应用问题。合理使用这些高级数据结构和相关算法是程序运行效率的关键因素,学好这门课会让你在之后的计算机专业课程以及项目设计中更得心应手,同时也将让你站在更高的角度去理解问题、设计程序。

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Çok değişkenli Fonksiyon II: Uygulamalar / Multivariable Calculus II: Applications

Çok değişkenli Fonksiyon II: Uygulamalar / Multivariable Calculus II: Applications

Ders çok değişkenli fonksiyonlardaki iki derslik dizinin ikincisidir. Birinci ders türev ve entegral kavramlarını geliştirmekte ve bu konulardaki problemleri temel çözme yöntemlerini sunmaktadır. Bu ders, birinci derste geliştirilen temeller üzerine daha ileri konuları işlemekte ve daha kapsamlı uygulamalar ve çözümlü örnekler sunmaktadır. Ders gerçek yaşamdan gelen uygulamaları da tanıtmaya önem veren “içerikli yaklaşımla” tasarlanmıştır. 

Bölümler
Bölüm 1: Multivar 1'in Özeti, Dairesel Koordinatlarda Entegraller
Bölüm 2: Türev Uygulamalarından Seçme Konular

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Introducción al análisis de Costos para la Dirección de Empresas

Introducción al análisis de Costos para la Dirección de Empresas

A través de un caso de negocios y de ejercicios prácticos tendrás la oportunidad de sumergirte en el desafiante mundo de los costos dentro de la empresa.
Para ello podrás complementar la visión estrictamente contable de los costos con una más enfocada a las decisiones recurrentes que se toman en el día a día de las empresas.
Para ello incorporaremos las distintas visiones del universo de los costos y cuál de ellas es útil para el tipo de decisión que tengas entre manos por un lado y como es la forma en que tenemos que sistematizar la construcción de los números por otro.

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Cómputo evolutivo

Cómputo evolutivo

La computación evolutiva (evolutionary computation, EC), aplica la teoría de la evolución natural y la genética en la adaptación evolutiva de estructuras computacionales, proporcionando un medio alternativo para atacar problemas complejos en diversas áreas, como la ingeniería, economía, química, medicina y, porque no, las artes.

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Contenido de las matemáticas de primaria

Contenido de las matemáticas de primaria

En este curso de acceso gratuito*, conocerás algunos temas de las matemáticas escolares con la profundidad necesaria para que puedas ayudar a tus estudiantes a aprenderlas.

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인공 지능 데이터 공정성 및 편향

인공 지능 데이터 공정성 및 편향

본 강의에서는 머신 러닝의 공정성과 편향에 대한 근본적인 문제를 배웁니다. 대학교 입학부터 대출 허가까지, 예측 모델을 이용해 만드는 중요한 결정이 늘어나면서, 모델이 불공정한 예측을 만들지 않게 하는 것이 굉장히 중요해졌습니다. 인간에서 비롯된 편향부터 데이터 세트 인지까지, 윤리적인 모델 구축의 여러 방면을 알아보겠습니다.

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頑想學概率:機率二 (Probability (2))

頑想學概率:機率二 (Probability (2))

這是一個機率的入門課程,著重的是教授機率基本概念。另外我們的作業將搭配臺大電機系所開發的多人競技線上遊戲方式,讓同學在遊戲中快樂的學習,快速培養同學們對於機率的洞察力與應用能力。

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1.- El Cálculo - Modelo Lineal

1.- El Cálculo - Modelo Lineal

Este curso forma parte de una secuencia con la que se propone un acercamiento a la Matemática Preuniversitaria que prepara para la Matemática Universitaria.
En él se asocia un significado real con el contenido matemático que se aprende y se integran tecnologías digitales en el proceso de aprendizaje.
El contexto real del movimiento en línea recta será el marco con el cual asociamos un primer significado y recapitulamos contenidos preuniversitarios relacionados con el Modelo Lineal.

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