《斯坦福大学-计算机软件学习课程》(Stanford Engineering Everywhere)更新CS106A 陆续更新中 [WMV]
中文名: 斯坦福大学-计算机软件学习课程
英文名: Stanford Engineering Everywhere
别名: 斯坦福大学-计算机软件学习课程
版本: 更新CS106A 陆续更新中 [WMV]
发行时间: 2008年
地区: 美国
对白语言: 英语
文字语言: 英文
简介:
简介:
斯坦福大学的“Stanford Engineering Everywhere ”免费提供学校里最受欢迎的工科课程，给全世界的学生和教育工作者。
得益于这个项目，我们有机会和全世界站在同一个数量级的知识起跑线上。
本课程系列内容来源于斯坦福大学的“Stanford Engineering Everywhere ”项目。
官网地址： http://see.stanford.edu/default.aspx
目前开设的课程有：
Introduction to Computer Science:
Programming Methodology CS106A
Programming Abstractions CS106B
Programming Paradigms CS107
Artificial Intelligence:
Introduction to Robotics CS223A
Natural Language Processing CS224N
Machine Learning CS229
Linear Systems and Optimization:
The Fourier Transform and its Applications EE261
Introduction to Linear Dynamical Systems EE263
Convex Optimization I EE364A
Convex Optimization II EE364B
.................
该资源包含的课程内容为:
Programming Methodology CS106A
Programming Abstractions CS106B
Programming Paradigms CS107
资源目前没有中文字幕 挑战一下吧!
目前更新课程: Programming Methodology CS106A
其余资源会陆续更新......
课程简介：
Introduction to Computer Science | Programming Methodology --CS106A
his course is the largest of the introductory programming courses and is one of the largest courses at Stanford. Topics focus on the introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing.
Programming Methodology teaches the widely-used Java programming language along with good software engineering principles. Emphasis is on good programming style and the built-in facilities of the Java language. The course is explicitly designed to appeal to humanists and social scientists as well as hard-core techies. In fact, most Programming Methodology graduates end up majoring outside of the School of Engineering.
Prerequisites: The course requires no previous background in programming, but does require considerable dedication and hard work.
Mehran Sahami
I joined the Computer Science Department at Stanford University as Associate Professor (Teaching), Associate Chair for Undergraduate Education, and Director of Educational Affairs. From 2001 to 2006, I also taught in the CS department at Stanford as a Lecturer. From 2002-2007, I was a Senior Research Scientist at Google, where I continue to maintain a consulting appointment in the research group. My research interests include computer science education, machine learning, and information retrieval on the Web. Please see my publications web page for more information.
Previously, I worked for several years as a Senior Engineering Manager at Epiphany. Prior to working at Epiphany, I completed my PhD in the Computer Science Department at Stanford. I was also an undergrad at Stanford and I loved it so much that I didn't want to leave.
Outside of work, I enjoy spending time with family, playing the guitar, going on outdoor excursions, and sleeping (which seems to be getting rarer and rarer these days).
Introduction to Computer Science | Programming Abstractions –CS106B
his course is the natural successor to Programming Methodology and covers such advanced programming topics as recursion, algorithmic analysis, and data abstraction using the C++ programming language, which is similar to both C and Java. If you've taken the Computer Science AP exam and done well (scored 4 or 5) or earned a good grade in a college course, Programming Abstractions may be an appropriate course for you to start with, but often Programming Abstractions (Accelerated) is a better choice. Programming Abstractions assumes that you already have familiarity with good programming style and software engineering issues (at the level of Programming Methodology), and that you can use this understanding as a foundation on which to tackle new topics in programming and data abstraction.
Topics: Abstraction and its relation to programming. Software engineering principles of data abstraction and modularity. Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. Uses the programming language C++ covering its basic facilities
Prerequisites: Solid performance in Programming Methodology and readiness to move on to advanced programming topics. A comparable introductory programming course (including high school AP courses) is often a reasonable substitute for our Programming Methodology.
Julie Zelenski
I left my rural hometown of Stevinson, CA (population: 262) to come to Stanford as a wide-eyed freshman in 1985. That tour passed through SLE, the LSJUMB, a half-dozen changes in my major, and I emerged with a Mathematical Sciences degree. A few years out in the "real world" were enough to send me running back for grad school in computer science and I segued into my current position as a lecturer in 1992 without setting foot off campus again. I teach courses in the undergrad systems curriculum, including programming methodology and abstractions, language paradigms, compilers, and object-oriented design and development, but I especially enjoy working with the section leaders in the CS106 courses. I have been the advisor to the Stanford SWE and ACM-W chapters and recently served on the Computer Science Advanced Placement development committee.
Introduction to Computer Science | Programming Paradigms --CS107
Advanced memory management features of C and C++; the differences between imperative and object-oriented paradigms. The functional paradigm (using LISP) and concurrent programming (using C and C++). Brief survey of other modern languages such as Python, Objective C, and C#.
Prerequisites:
Programming and problem solving at the
Programming Abstractions level. Prospective students should know a reasonable amount of C++. You should be comfortable with arrays, pointers, references, classes, methods, dynamic memory allocation, recursion, linked lists, binary search trees, hashing, iterators, and function pointers. You should be able to write well-decomposed, easy-to-understand code, and understand the value that comes with good variable names, short function and method implementations, and thoughtful, articulate comments.
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