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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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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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面向对象技术高级课程(The Advanced Object-Oriented Technology)

面向对象技术高级课程(The Advanced Object-Oriented Technology)

如果将软件开发人员看作行走江湖的武侠,那么各种软件开发技术就是武侠赖以傍身的武术秘籍。有的秘籍以易学灵巧见长,练习者很快掌握,并有立竿见影的功效,但失之厚重持久;有的秘籍初学乍练顿显困难,虽冥思苦解仍不得要领,需坚持不断的内功修炼方成正果,却是成为武林大侠必备的至宝。本课程有些类似于后者。

《面向对象技术高级课程》深入、系统、完整地讲解当今主流的面向对象软件开发方法的分析、设计、实现及重构方法,深入讲解UML语言的高级技术细节,以及近年来面向对象方法最新的发展趋势。课程集百家之所言,并结合主讲者最新的研究成果,并通过大量、丰富、完整、不同领域、应用不同技术的案例将其中的关键知识点串联起来,便于理解和应用。

本课程并不拘限于某一特定的面向对象的编程语言的编程技巧,但却包含了基于当前主流的面向对象编程语言的多项完整案例。本课程引导选课同学从更为抽象和共性的模型角度描述现实世界和软件世界,即构建分析与设计模型,之后再将其落实到软件编程实现。这种工程化的理念对于开发大型、复杂、困难、攸关软件系统是非常必要的,也是作为高级程序员、系统分析师、软件架构师等高级软件技术职位所必需具有的素质。

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算法基础

算法基础

算法代表着用系统的方法描述解决问题的策略机制,北京大学《算法基础》课程将带你一一探索枚举、二分、贪心、递归、深度优先搜索、广度优先搜索、动态规划等经典算法,体会他们巧妙的构思,感受他们利用计算解决问题的独特魅力。顺利完成本课程,你将不但能够掌握这些算法的原理,还能够对这些算法进行灵活应用以及准确实现。本课程的中的编程任务,将充分训练你的思维能力和动手能力,促成全面、缜密思考问题的习惯。达到本门课程的要求,即意味者你具备了初步的算法基础和较强的编程实现能力。

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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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离散数学概论 Discrete Mathematics Generality

离散数学概论 Discrete Mathematics Generality

离散数学是计算机科学的基础理论,离散结构的基础知识和逻辑思维的形式化是信息技术类学生的基本功,离散数学的基本概念是理科专业学生进行信息类课程学习的重要基础。

本课程介绍计算机科学和信息技术理论基础的概念和思想方法,介绍数理逻辑、集合论、图论、抽象代数和形式语言与自动机等各部分的基本概念,介绍离散数学基本概念和空间信息技术之间的联系与结合,培养学生理解和掌握离散数学基本概念,采用形式化方法分析问题,并能自觉运用逻辑分析、结构层次分析和同构类比等思想方法解决问题的能力。

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离散数学

离散数学

离散数学是计算机专业基础课程之一。本课程重点讲解离散对象的计数问题、图论问题、网络流问题等计算机科学中的重要基本知识。本课程将为后续如数据结构、算法设计、复杂性理论提供必要的基础,课程内容在人工智能、机器学习、网络设计与分析等前沿领域有重要应用。课程重点培养学生逻辑思维能力及解决问题的能力。

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数据结构基础

数据结构基础

学了C/C++ 语言,我们已经会编程解题了,那怎么用来处理实际的问题呢?
怎么设计数据结构来有效地管理企业人员?如何编写程序没让人才和岗位达到最佳匹配?如何安排旅行计划,找到最佳行程路径?这些学习、工作、生活中常常困扰我们的问题,你将在《数据结构基础》课程中找到答案。
数据结构是计算机程序设计的基础,告诉我们数据在计算机里是如何组织与存储的,能帮助我们刻画实际问题中的信息及其相互关系。当我们对一个实际问题本身有了一个结构化的认识之后,就可以选择合适的解决方法了。
完成这门课之时,你将掌握线性表、栈与队列、字符串、二叉树、树、图等经典的数据结构,并运用这些基础数据结构和相关算法解决实际应用问题。扎实的数据结构知识是项目开发与结构层次设计的关键,学好这门课会让你在高年级的计算机专业课程以及毕业项目设计中事半功倍,同时也将为你成为一名优秀的程序设计人员打下坚实的基础。

附:本课程的内容来自教材:《数据结构与算法》(张铭、王腾蛟、赵海燕 编著),高等教育出版社,2008年。

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人工智慧:搜尋方法與邏輯推論 (Artificial Intelligence - Search & Logic)

人工智慧:搜尋方法與邏輯推論 (Artificial Intelligence - Search & Logic)

本課程分為人工智慧(上)、人工智慧(下)兩部份,第一部分除了人工智慧概論外,著重在目標搜尋、meta heuristic、電腦對弈、演繹學習(包含證言邏輯、一階邏輯及 planning )等技術。這些技術主要發展時機為人工智慧的第一波及第二波熱潮,也就是 1950 年代至 1990 年代附近的主流發展,即使到現在也在各個領域廣為應用。

課程教學目標:
使同學對人工智慧有基礎概念
同學能夠理解如何運用目標搜尋技術及演繹學習方式達成人工智慧
同學能將相關技術應用到自己的問題上

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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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Vector Calculus for Engineers

Vector Calculus for Engineers

This course covers both the theoretical foundations and practical applications of Vector Calculus. During the first week, students will learn about scalar and vector fields. In the second week, they will differentiate fields. The third week focuses on multidimensional integration and curvilinear coordinate systems. Line and surface integrals are covered in the fourth week, while the fifth week explores the fundamental theorems of vector calculus, including the gradient theorem, the divergence theorem, and Stokes' theorem.

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Trigonometría y geometría analítica

Trigonometría y geometría analítica

El curso Trigonometría y geometría analítica abordará en su primer módulo el estudio de la trigonometría y sus aplicaciones en casos prácticos. A partir del segundo módulo abordará el estudio de la geometría analítica, en particular, de las secciones cónicas: la recta, la parábola, la circunferencia y la elipse; así como sus aplicaciones en situaciones de la vida cotidiana.

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Trees, SVM and Unsupervised Learning

Trees, SVM and Unsupervised Learning

"Trees, SVM and Unsupervised Learning" is designed to provide working professionals with a solid foundation in support vector machines, neural networks, decision trees, and XG boost. Through in-depth instruction and practical hands-on experience, you will learn how to build powerful predictive models using these techniques and understand the advantages and disadvantages of each. The course will also cover how and when to apply them to different scenarios, including binary classification and K > 2 classes.

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The Power of Statistics

The Power of Statistics

This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional.

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  • 37 hours
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Statistics for Genomic Data Science

Statistics for Genomic Data Science

An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University.

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Statistics for Data Science with Python

Statistics for Data Science with Python

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis.

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  • 14 hours
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Statistics

Statistics

This course provides an overview of bachelor-level statistics. You will review the concepts of descriptive and inferential statistics. You will use the statistical software package R on real data to gain insight in these topics.

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Statistical Inference for Estimation in Data Science

Statistical Inference for Estimation in Data Science

This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings.

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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
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Single Variable Calculus

Single Variable Calculus

Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with emphases on conceptual understanding and applications. The course is ideal for students beginning in the engineering, physical, and social sciences.

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  • 14 hours
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Simulation Models for Decision Making

Simulation Models for Decision Making

This course is primarily aimed at third- and fourth-year undergraduate students or graduate students interested in learning simulation techniques to solve business problems.

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Sesenta años de inteligencia artificial

Sesenta años de inteligencia artificial

En este curso, ofrecido por la UNAM, cubriremos el pasado, presente y futuro de la inteligencia artificial. También mencionaremos los conceptos más importantes que serán útiles en el resto del programa especializado. Discutiremos las implicaciones sociales, éticas y filosóficas de los desarrollos en inteligencia artificial.

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Response Surfaces, Mixtures, and Model Building

Response Surfaces, Mixtures, and Model Building

Factorial experiments are often used in factor screening.; that is, identify the subset of factors in a process or system that are of primary important to the response. Once the set of important factors are identified interest then usually turns to optimization; that is, what levels of the important factors produce the best values of the response. This course provides design and optimization tools to answer that questions using the response surface framework.

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Resolución de problemas por búsqueda

Resolución de problemas por búsqueda

El curso trata de resolución automática de problemas por medio de algoritmos de búsqueda.
Aprenderás a abstraer un problema como un grafo de estados-acciones y a dimensionar su complejidad por medio de la identificación de parámetros. Además, te mostraremos cómo analizar el consumo de recursos computacionales de los algoritmos para seleccionar o adaptar el más apropiado al problema.
Nos interesa que puedas aplicar los algoritmos a problemas concretos.

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Resampling, Selection and Splines

Resampling, Selection and Splines

"Statistical Learning for Data Science" is an advanced course designed to equip working professionals with the knowledge and skills necessary to excel in the field of data science. Through comprehensive instruction on key topics such as shrink methods, parametric regression analysis, generalized linear models, and general additive models, students will learn how to apply resampling methods to gain additional information about fitted models, optimize fitting procedures to improve prediction accuracy and interpretability, and identify the benefits and approach of non-linear models.

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  • 16 hours
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