计算机科学 第45页

课程名称（英文）：Interaction Design Specialization

课程名称（中文）：交互设计专项课程

课程链接：https://www.coursera.org/specializations/interaction-design

平台：Coursera

大学或机构：University of California San Diego/加州大学圣地亚哥分校

课程介绍：

You will learn how to design technologies that bring people joy, rather than frustration. You’ll learn how to generate design ideas, techniques for quickly prototyping them, and how to use prototypes to get feedback from other stakeholders like your teammates, clients, and users. You’ll also learn principles of visual design, perception, and cognition that inform effective interaction design.

交互设计专业：您将学习如何设计使人们感到高兴而不是沮丧的技术。您将学习如何生成设计思路，快速进行原型制作的技术，以及如何使用原型来获取其他利益相关者（如队友，客户和用户）的反馈。您还将学习视觉设计，感知和认知的原理，这些原理可指导有效的交互设计。

包含课程详情（点击对应课程后方详情了解对应课程具体信息）：

1、Human-Centered Design: an Introduction/以人为本的设计:概述（详情）
2、Design Principles: an Introduction 设计原理：概述（详情）
3、Social Computing 社会计算（详情）
4、Input and Interaction /输入与互动（详情）
5、User Experience: Research & Prototyping/用户体验:研究与原型（详情）
6、Information Design /信息设计（详情）
7、Designing, Running, and Analyzing Experiments /设计，运行和实验分析（详情）
8、Interaction Design Capstone Project（详情）

课程视频压缩包下载（度盘链接 解压密码：xuebuyan.org）：

课程名称（英文）：Functional Programming in Scala Specialization

课程名称（中文）：Functional Programming in Scala 专项课程

课程链接：https://www.coursera.org/specializations/scala

平台：Coursera

大学或机构：洛桑联邦理工学院

课程介绍：

Discover how to write elegant code that works the first time it is run. This Specialization provides a hands-on introduction to functional programming using the widespread programming language, Scala. It begins from the basic building blocks of the functional paradigm, first showing how to use these blocks to solve small problems, before building up to combining these concepts to architect larger functional programs. You’ll see how the functional paradigm facilitates parallel and distributed programming, and through a series of hands on examples and programming assignments, you’ll learn how to analyze data sets small to large; from parallel programming on multicore architectures, to distributed programming on a cluster using Apache Spark. A final capstone project will allow you to apply the skills you learned by building a large data-intensive application using real-world data.

Scala专业化中的函数式编程：发现如何编写优雅的代码，使其在首次运行时就可以工作。 该专业知识提供了使用广泛的编程语言Scala进行功能编程的动手入门。它从功能范式的基本构建模块开始，首先展示了如何使用这些模块来解决小问题，然后再构建以将这些概念组合起来以构建更大的功能程序。您将看到功能范式如何促进并行和分布式编程，并通过一系列动手操作示例和编程分配，将学习如何分析从小到大的数据集。从多核体系结构上的并行编程到使用Apache Spark在集群上的分布式编程。最终的顶点项目将使您能够运用通过使用实际数据构建大型数据密集型应用程序而学到的技能。

包含课程详情（点击对应课程后方详情了解对应课程具体信息）：

1、Functional Programming Principles in Scala-Scala 函数式程序设计原理（详情）
2、Functional Program Design in Scala（详情）
3、Parallel programming 并行编程（详情）
4、Big Data Analysis with Scala and Spark（详情）
5、Functional Programming in Scala Capstone（详情）

课程视频压缩包下载（度盘链接）：

课程名称（英文）：Software Product Management Specialization

课程名称（中文）：软件产品管理专项课程

课程链接：https://www.coursera.org/specializations/product-management

平台：Coursera

大学或机构：University of Alberta/阿尔伯塔大学

课程介绍：软件产品管理专业知识：在此软件产品管理专业知识中，您将掌握敏捷软件管理实践，以领导一组开发人员并与客户进行交互。在最后的Capstone项目中，您将练习管理技术并将其应用于作为软件产品经理将要面对的现实情况。作为软件产品管理的一部分，您将有机会分享您的经验并学习他人的见解。

包含课程详情（点击对应课程后方详情了解对应课程具体信息）：

1、Introduction to Software Product Management 软件产品管理导论（详情）
2、Software Processes and Agile Practices 软件开发过程与敏捷开发实践（详情）
3、Client Needs and Software Requirements 客户要求与软件需求（详情）
4、Agile Planning for Software Products 软件产品的敏捷规划（详情）
5、Reviews & Metrics for Software Improvements 软件进步的评价与指标（详情）
6、Software Product Management Capstone /软件产品管理毕业项目(详情)）

课程视频压缩包下载（度盘链接）：

课程名称: Software Product Management Capstone /软件产品管理毕业项目

课程主页: https://www.coursera.org/learn/software-product-management-capstone

所在平台: Coursera

课程类别: 计算机科学

大学或机构: 阿尔伯塔大学

讲师： Kenny Wong

授课语言： 英语

提供字幕： 英文

课程文件大小： 478MB

课程介绍：

软件产品管理的顶峰：在这个为期六周的顶峰课程中，您将在安全，模拟的软件生产环境中获得实践管理经验。您将运用敏捷实践和技术来克服行业引发的挑战。与现实的客户进行交互，您将了解他们的需求，并表达他们在推动软件生产的软件需求中真正需要的东西。完成顶峰之后，您将准备好作为自信的软件产品管理专业人员来发展自己的职业。

此课程属于 Software Product Management Specialization/ 软件产品管理专项课程 中的第6门课程。

课程压缩包下载地址（度盘链接）：

课程名称（英文）：Applied Data Science with Python Specialization

课程名称（中文）：Applied Data Science with Python 专项课程

课程链接：https://www.coursera.org/specializations/data-science-python

平台：Coursera

大学或机构：University of Michigan/密歇根大学

课程介绍：面向所有人的Python专业知识：此专业基于Python面向所有人课程的成功，将介绍基本的编程概念，包括使用Python编程语言的数据结构，网络应用程序接口和数据库。在Capstone项目中，您将使用在整个专业化过程中学到的技术来设计和创建自己的应用程序，以进行数据检索，处理和可视化。

包含课程详情（点击对应课程后方详情了解对应课程具体信息）：

1、Introduction to Data Science in Python（详情）
2、Applied Plotting, Charting & Data Representation in Python（详情）
3、Applied Machine Learning in Python（详情）
4、Applied Text Mining in Python（详情）
5、Applied Social Network Analysis in Python（详情）

课程视频压缩包下载（度盘链接）：

课程名称（英文）：Python for Everybody Specialization

课程名称（中文）：零基础 Python 入门 专项课程

课程链接：https://www.coursera.org/specializations/python

平台：Coursera

大学或机构：密歇根大学

课程介绍：面向所有人的Python专业知识：此专业基于Python面向所有人课程的成功，将介绍基本的编程概念，包括使用Python编程语言的数据结构，网络应用程序接口和数据库。在Capstone项目中，您将使用在整个专业化过程中学到的技术来设计和创建自己的应用程序，以进行数据检索，处理和可视化。

包含课程详情（点击对应课程后方详情了解对应课程具体信息）：

1、Programming for Everybody-大家的编程（开始学习python) （详情）
2、Python Data Structures-Python 数据结构 （详情）
3、Using Python to Access Web Data-使用 Python 访问网络数据 （详情）
4、Using Databases with Python-Python 数据库开发 （详情）
5、Capstone-Retrieving, Processing, and Visualizing Data with Python（详情）

课程视频压缩包下载（度盘链接）：
给您发的是两个版本，分别是Python 2.X和3.X版本，python2.x版本课程资料只有视频和字幕，官方提供的中文字幕相对多些；python3.x版本课程资料有高清视频字幕和课件以及测试作业等，但官网提供的中文字幕相对较少。亲可以按需下载，推荐两个版本都下载。

课程名称: 程序语言设计 华盛顿大学 Dan Grossman

课程主页: 官网已下架

所在平台: Coursera

课程类别: 计算机科学

大学或机构: 华盛顿大学

讲师： Dan Grossman

授课语言： 英语

提供字幕： 英语

课程文件大小： 1.32GB

课程介绍： Learn many of the concepts that underlie all programming languages. Develop a programming style known as functional programming and contrast it with object-oriented programming. Through experience writing programs and studying three different languages, learn the key issues in designing and using programming languages, such as modularity and the complementary benefits of static and dynamic typing. This course is neither particularly theoretical nor just about programming specifics – it will give you a framework for understanding how to use language constructs effectively and how to design correct and elegant programs. By using different languages, you learn to think more deeply than in terms of the particular syntax of one language. The emphasis on functional programming is essential for learning how to write robust, reusable, composable, and elegant programs – in any language.

课程压缩包下载地址（度盘链接）：

课程名称: Probabilistic Graphical Models 概率图模型

课程主页: 官网已下架

所在平台: Coursera

课程类别: 计算机科学

大学或机构: 斯坦福大学

讲师： Daphne Koller

授课语言： 英语

提供字幕： 英语

课程文件大小： 1.52GB

课程介绍：

What are Probabilistic Graphical Models?

Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which spans methods such as Bayesian networks and Markov random fields, uses ideas from discrete data structures in computer science to efficiently encode and manipulate probability distributions over high-dimensional spaces, often involving hundreds or even many thousands of variables. These methods have been used in an enormous range of application domains, which include: web search, medical and fault diagnosis, image understanding, reconstruction of biological networks, speech recognition, natural language processing, decoding of messages sent over a noisy communication channel, robot navigation, and many more. The PGM framework provides an essential tool for anyone who wants to learn how to reason coherently from limited and noisy observations.

In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques; you will also learn algorithms for using a PGM to reach conclusions about the world from limited and noisy evidence, and for making good decisions under uncertainty. The class covers both the theoretical underpinnings of the PGM framework and practical skills needed to apply these techniques to new problems.

课程大纲

Topics covered include:

1. The Bayesian network and Markov network representation, including extensions for reasoning over domains that change over time and over domains with a variable number of entities
2. Reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods)
3. Learning parameters and structure in PGMs
4. Using a PGM for decision making under uncertainty.

There will be short weekly review quizzes and programming assignments (Octave/Matlab) focusing on case studies and applications of PGMs to real-world problems:

1. Credit Scoring and Factors
2. Modeling Genetic Inheritance and Disease
3. Markov Networks and Optical Character Recognition (OCR)
4. Inference: Belief Propagation
5. Markov Chain Monte Carlo and Image Segmentation
6. Decision Theory: Arrhythmogenic Right Ventricular Dysplasia
7. Conditional Random Field Learning for OCR
8. Structure Learning for Identifying Skeleton Structure
9. Human Action Recognition with Kinect

To prepare for the class in advance, you may consider reading through the following sections of the textbook (discount code DKPGM12) by Daphne and Nir Friedman:

1. Introduction and Overview. Chapters 1, 2.1.1 – 2.1.4, 4.2.1.
2. Bayesian Network Fundamentals. Chapters 3.1 – 3.3.
3. Markov Network Fundamentals. Chapters 4.1, 4.2.2, 4.3.1, 4.4, 4.6.1.
4. Structured CPDs. Chapters 5.1 – 5.5.
5. Template Models. Chapters 6.1 – 6.4.1.

These will be covered in the first two weeks of the online class.

课程压缩包下载地址（度盘链接）：

课程名称: 自动机理论 Automata 斯坦福大学 Jeff Ullman

课程主页: 官网已下架

所在平台: Coursera

课程类别: 计算机科学

大学或机构: 斯坦福大学

讲师： Jeff Ullman

授课语言： 英语

提供字幕： 英语

课程文件大小： 760MB

课程介绍：

I am pleased to be able to offer free over the Internet a course on Automata Theory, based on the material I have taught periodically at Stanford in the course CS154. Students have access to screencast lecture videos, are given quiz questions, assignments and exams, receive regular feedback on progress, and can participate in a discussion forum. Those who successfully complete the course will receive a statement of accomplishment. You will need a decent Internet connection for accessing course materials, but should be able to watch the videos on your smartphone.

The course covers four broad areas: (1) Finite automata and regular expressions, (2) Context-free grammars, (3) Turing machines and decidability, and (4) the theory of intractability, or NP-complete problems.

Why Study Automata Theory?

This subject is not just for those planning to enter the field of complexity theory, although it is a good place to start if that is your goal. Rather, the course will emphasize those aspects of the theory that people really use in practice. Finite automata, regular expressions, and context-free grammars are ideas that have stood the test of time. They are essential tools for compilers. But more importantly, they are used in many systems that require input that is less general than a full programming language yet more complex than “push this button.”

The concepts of undecidable problems and intractable problems serve a different purpose. Undecidable problems are those for which no computer solution can ever exist, while intractable problems are those for which there is strong evidence that, although they can be solved by a computer, they cannot be solved sufficiently fast that the solution is truly useful in practice. Understanding this theory, and in particular being able to prove that a problem you are facing belongs to one of these classes, allows you to justify taking another approach — simplifying the problem or writing code to approximate the solution, for example.

During the course, I’m going to prove a number of things. The purpose of these proofs is not to torture you or confuse you. Neither are the proofs there because I doubt you would believe me were I merely to state some well-known fact. Rather, understanding how these proofs, especially inductive proofs, work, lets you think more clearly about your own work. I do not advocate proofs that programs are correct, but whenever you attempt something a bit complex, it is good to have in mind the inductive proofs that would be needed to guarantee that what you are doing really works in all cases.

课程压缩包下载地址（度盘链接）：

课程名称:Designing and Executing Information Security Strategies

课程主页: 官网已下架

所在平台: Coursera

课程类别: 计算机科学

大学或机构: 华盛顿大学

讲师： Mike Simon

授课语言： 英语

提供字幕： 英语

课程文件大小： 929MB

课程介绍： This course provides you with opportunities to integrate and apply your information security knowledge. Following the case-study approach, you will be introduced to current, real-world cases developed and presented by the practitioner community. You will design and execute information assurance strategies to solve these cases.

课程压缩包下载地址（度盘链接）：