S
Stonelesscutter
Guest
Stanford University offers a variety of courses that can be followed online completely free of charge. I've been taking the course "Introduction to Databases" myself for a while now and am pleasantly surprised with it. It is not possible at this time to join this course any longer but there's a good chance it will be offered again somewhere in fall 2012. There is however an extended range of courses, most of which start in January. Many courses are from the Computer Science department, but I'm sure there is something for everyone to enjoy here, so sign up before it's too late. 
The Lean Launchpad (Entrepreneurship)
"In this class you'll learn how to turn a great idea into a great company. We now know that startups are not smaller versions of large companies. Large companies execute known business models. They use big company tools - business plans, income statements, revenue models, etc. to help organized their execution. In contrast startups search for a business model. And all the big company tools are irrelevant in the early days of a startup. This class is not about how to write a business plan. It's not an exercise on how smart you are in a classroom, or how well you use the research library. The end result is not a PowerPoint slide deck for a VC presentation. Instead you will be getting your hands dirty as you encounter the chaos and uncertainty of how a startup actually works. You'll learn how to use a business model canvas to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone other than you would want/use your product. Finally, you'll see how agile development can help you rapidly iterate your product to build something customers will use and buy. Each week will be a new adventure as you test each part of your business model."
Instructor: Steve Blank.
Technology Entrepreneurship (Entrepreneurship)
"How do you create a successful start-up? What is entrepreneurial leadership in a large firm? What are the differences between an idea and true opportunity? How does an entrepreneur form a team and gather the resources necessary to create a great enterprise? This class mixes in-depth case studies and research on the entrepreneurial process. For undergraduates of all majors who seek to understand the formation and growth of high-impact start-ups in areas such as information, green/clean, medical and consumer technologies. No prerequisites are necessary but the course is targeted at Juniors and Seniors."
Instructor: Chuck Eesley.
Anatomy (Medicine)
"Anatomy is the most fundamental of all medical sciences. It provides the basic framework and vocabulary used in all descriptions and communications about the body. Anyone studying or working in the healthcare area or collaborating with the medical field will find this course of immense value. A variety of multimedia resources will be used to provide lucid descriptions of complex topics. Explicit learning objectives will be stated and embedded quizzes will aid in assessment. Students will be able to complete this course in a self-paced manner. This course will cover the region of upper limb. Subsequent courses will cover other body regions in a sequential manner."
Instructor: Sakti Srivastava.
Making Green Buildings (Civil Engineering)
"This class is for students who are passionate about improving the sustainability of the built environment and want to learn about how to manage project teams to design and build sustainable buildings. Sustainable buildings do more for their users and they are built productively and with environmental and social sensitivity. This can only happen if everybody involved in making and using a building participates when their input is most useful and if the whole process is managed well. Such an integrated and holistic project management approach requires a new mindset and novel methods, which are taught in the class."
Instructor: Martin Fisher.
Information Theory (Electrical Engr.)
"Information theory is the science of operations on data such as compression, storage, and communication. It is among the few disciplines fortunate to have a precise date of birth: 1948, with the publication of Claude E. Shannon's paper entitled "A Mathematical Theory of Communication". Our course will explore the basic concepts of Information theory. It is a prerequisite for research in this area, and highly recommended for students planning to delve into the fields of communications, data compression, and statistical signal processing. The intimate acquaintance that we will gain with measures of information and uncertainty - such as mutual information, entropy, and relative entropy - would be invaluable also for students, researchers, and practitioners in fields ranging from neuroscience to machine learning. Also encouraged to enroll are students of statistics and probability, who will gain an appreciation for the interplay between information theory, combinatorics, probability, and statistics."
Instructor: Tsachy Weissman.
Model Thinking (Complex Systems)
"Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians."
Instructor: Scott E. Page.
CS 101 (Computer Science)
"CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. CS101 demystifies and brings those patterns to life, which is useful for anyone using computers today. In CS101, students play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers. Everything works within the browser, so there is no extra software to download or install. CS101 also provides a general background on computers today: what is a computer, what is hardware, what is software, what is the internet. No previous experience is required other than the ability to use a web browser."
Instructor: Nick Parlante.
Machine Learning (Computer Science)
"Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI."
Instructor: Andrew Ng.
Software Engineering for Software as a Service (Computer Science)
"This course teaches the engineering fundamentals for long-lived software using the highly-productive Agile development method for Software as a Service (SaaS) using Ruby on Rails. Agile developers continuously refine and refactor a working but incomplete prototype until the customer is happy with result, with the customer offering continuous feedback. Agile emphasizes user stories to validate customer requirements; test-driven development to reduce mistakes; biweekly iterations of new software releases; and velocity to measure progress. We will introduce all these elements of the Agile development cycle, and go through one iteration by adding features to a simple app and deploying it on the cloud using tools like Github, Cucumber, RSpec, RCov, Pivotal Tracker, and Heroku."
Instructors: Armando Fox, David Patterson.
Human-Computer Interaction (Computer Science)
"In this course, you will learn how to design technologies that bring people joy, rather than frustration. You'll learn several techniques for rapidly prototyping and evaluating multiple interface alternatives — and why rapid prototyping and comparative evaluation are essential to excellent interaction design. You'll learn how to conduct fieldwork with people to help you get design ideas. How to make paper prototypes and low-fidelity mock-ups that are interactive -- and how to use these designs to get feedback from other stakeholders like your teammates, clients, and users. You'll learn principles of visual design so that you can effectively organize and present information with your interfaces. You'll learn principles of perception and cognition that inform effective interaction design. And you'll learn how to perform and analyze controlled experiments online."
Instructor: Scott Klemmer.
Natural Language Processing (Computer Science)
"Natural language processing is the technology for dealing with our most ubiquitous product: human language, as it appears in emails, web pages, tweets, product descriptions, newspaper stories, social media, and scientific articles, in thousands of languages and varieties. In the past decade, successful natural language processing applications have become part of our everyday experience, from spelling and grammar correction in word processors to machine translation on the web, from email spam detection to automatic question answering, from detecting people's opinions about products or services to extracting appointments from your email. In this class, you'll learn the fundamental algorithms and mathematical models for human language processing and how you can use them to solve practical problems in dealing with language data wherever you encounter it."
Instructors: Dan Jurafsky, Christopher Manning.
Game Theory (Computer Science)
"Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call 'games' in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE. How could you begin to model eBay, Google keyword auctions, and peer to peer file-sharing networks, without accounting for the incentives of the people using them? The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We'll include a variety of examples including classic games and a few applications."
Instructors: Matthew O. Jackson, Yoav Shoham.
Probabilistic Graphical Models (Computer Science)
"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. Topics include: (i) 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; (ii) reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods); (iii) learning methods for both parameters and structure in a PGM; (iv) using a PGM for decision making under uncertainty. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply PGM methods to computer vision, text understanding, medical decision making, speech recognition, and many other areas."
Instructor: Daphne Koller.
Cryptography (Computer Science)
"Cryptography is an indispensable tool for protecting information in computer systems. This course explains the inner workings of cryptographic primitives and how to correctly use them. Students will learn how to reason about the security of cryptographic constructions and how to apply this knowledge to real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two or more parties generate a shared secret key. We will cover the relevant number theory and discuss public-key encryption, digital signatures, and authentication protocols. Towards the end of the course we will cover more advanced topics such as zero-knowledge, distributed protocols such as secure auctions, and a number of privacy mechanisms. Throughout the course students will be exposed to many exciting open problems in the field."
Instructor: Dan Boneh.
Design and Analysis of Algorithms I (Computer Science)
"In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?"
Instructor: Tim Roughgarden.
Computer Security (Computer Science)
"In this class you will learn how to design secure systems and write secure code. You will learn how to find vulnerabilities in code and how to design software systems that limit the impact of security vulnerabilities. We will focus on principles for building secure systems and give many real world examples. In addition, the course will cover topics such as:
Instructors: Dan Boneh, John Mitchell, Dawn Song.
The Lean Launchpad (Entrepreneurship)
"In this class you'll learn how to turn a great idea into a great company. We now know that startups are not smaller versions of large companies. Large companies execute known business models. They use big company tools - business plans, income statements, revenue models, etc. to help organized their execution. In contrast startups search for a business model. And all the big company tools are irrelevant in the early days of a startup. This class is not about how to write a business plan. It's not an exercise on how smart you are in a classroom, or how well you use the research library. The end result is not a PowerPoint slide deck for a VC presentation. Instead you will be getting your hands dirty as you encounter the chaos and uncertainty of how a startup actually works. You'll learn how to use a business model canvas to brainstorm each part of a company and customer development to get out of the classroom to see whether anyone other than you would want/use your product. Finally, you'll see how agile development can help you rapidly iterate your product to build something customers will use and buy. Each week will be a new adventure as you test each part of your business model."
Instructor: Steve Blank.
Technology Entrepreneurship (Entrepreneurship)
"How do you create a successful start-up? What is entrepreneurial leadership in a large firm? What are the differences between an idea and true opportunity? How does an entrepreneur form a team and gather the resources necessary to create a great enterprise? This class mixes in-depth case studies and research on the entrepreneurial process. For undergraduates of all majors who seek to understand the formation and growth of high-impact start-ups in areas such as information, green/clean, medical and consumer technologies. No prerequisites are necessary but the course is targeted at Juniors and Seniors."
Instructor: Chuck Eesley.
Anatomy (Medicine)
"Anatomy is the most fundamental of all medical sciences. It provides the basic framework and vocabulary used in all descriptions and communications about the body. Anyone studying or working in the healthcare area or collaborating with the medical field will find this course of immense value. A variety of multimedia resources will be used to provide lucid descriptions of complex topics. Explicit learning objectives will be stated and embedded quizzes will aid in assessment. Students will be able to complete this course in a self-paced manner. This course will cover the region of upper limb. Subsequent courses will cover other body regions in a sequential manner."
Instructor: Sakti Srivastava.
Making Green Buildings (Civil Engineering)
"This class is for students who are passionate about improving the sustainability of the built environment and want to learn about how to manage project teams to design and build sustainable buildings. Sustainable buildings do more for their users and they are built productively and with environmental and social sensitivity. This can only happen if everybody involved in making and using a building participates when their input is most useful and if the whole process is managed well. Such an integrated and holistic project management approach requires a new mindset and novel methods, which are taught in the class."
Instructor: Martin Fisher.
Information Theory (Electrical Engr.)
"Information theory is the science of operations on data such as compression, storage, and communication. It is among the few disciplines fortunate to have a precise date of birth: 1948, with the publication of Claude E. Shannon's paper entitled "A Mathematical Theory of Communication". Our course will explore the basic concepts of Information theory. It is a prerequisite for research in this area, and highly recommended for students planning to delve into the fields of communications, data compression, and statistical signal processing. The intimate acquaintance that we will gain with measures of information and uncertainty - such as mutual information, entropy, and relative entropy - would be invaluable also for students, researchers, and practitioners in fields ranging from neuroscience to machine learning. Also encouraged to enroll are students of statistics and probability, who will gain an appreciation for the interplay between information theory, combinatorics, probability, and statistics."
Instructor: Tsachy Weissman.
Model Thinking (Complex Systems)
"Evidence shows that people who think with models consistently outperform those who don't. And, moreover people who think with lots of models outperform people who use only one. Why do models make us better thinkers? Models help us to better organize information - to make sense of that fire hose or hairball of data (choose your metaphor) available on the Internet. Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures. In this class, I present a starter kit of models: I start with models of tipping points. I move on to cover models explain the wisdom of crowds, models that show why some countries are rich and some are poor, and models that help unpack the strategic decisions of firm and politicians."
Instructor: Scott E. Page.
CS 101 (Computer Science)
"CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. Computers can appear very complicated, but in reality, computers work within just a few, simple patterns. CS101 demystifies and brings those patterns to life, which is useful for anyone using computers today. In CS101, students play and experiment with short bits of "computer code" to bring to life to the power and limitations of computers. Everything works within the browser, so there is no extra software to download or install. CS101 also provides a general background on computers today: what is a computer, what is hardware, what is software, what is the internet. No previous experience is required other than the ability to use a web browser."
Instructor: Nick Parlante.
Machine Learning (Computer Science)
"Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI."
Instructor: Andrew Ng.
Software Engineering for Software as a Service (Computer Science)
"This course teaches the engineering fundamentals for long-lived software using the highly-productive Agile development method for Software as a Service (SaaS) using Ruby on Rails. Agile developers continuously refine and refactor a working but incomplete prototype until the customer is happy with result, with the customer offering continuous feedback. Agile emphasizes user stories to validate customer requirements; test-driven development to reduce mistakes; biweekly iterations of new software releases; and velocity to measure progress. We will introduce all these elements of the Agile development cycle, and go through one iteration by adding features to a simple app and deploying it on the cloud using tools like Github, Cucumber, RSpec, RCov, Pivotal Tracker, and Heroku."
Instructors: Armando Fox, David Patterson.
Human-Computer Interaction (Computer Science)
"In this course, you will learn how to design technologies that bring people joy, rather than frustration. You'll learn several techniques for rapidly prototyping and evaluating multiple interface alternatives — and why rapid prototyping and comparative evaluation are essential to excellent interaction design. You'll learn how to conduct fieldwork with people to help you get design ideas. How to make paper prototypes and low-fidelity mock-ups that are interactive -- and how to use these designs to get feedback from other stakeholders like your teammates, clients, and users. You'll learn principles of visual design so that you can effectively organize and present information with your interfaces. You'll learn principles of perception and cognition that inform effective interaction design. And you'll learn how to perform and analyze controlled experiments online."
Instructor: Scott Klemmer.
Natural Language Processing (Computer Science)
"Natural language processing is the technology for dealing with our most ubiquitous product: human language, as it appears in emails, web pages, tweets, product descriptions, newspaper stories, social media, and scientific articles, in thousands of languages and varieties. In the past decade, successful natural language processing applications have become part of our everyday experience, from spelling and grammar correction in word processors to machine translation on the web, from email spam detection to automatic question answering, from detecting people's opinions about products or services to extracting appointments from your email. In this class, you'll learn the fundamental algorithms and mathematical models for human language processing and how you can use them to solve practical problems in dealing with language data wherever you encounter it."
Instructors: Dan Jurafsky, Christopher Manning.
Game Theory (Computer Science)
"Popularized by movies such as "A Beautiful Mind", game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. Beyond what we call 'games' in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE. How could you begin to model eBay, Google keyword auctions, and peer to peer file-sharing networks, without accounting for the incentives of the people using them? The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We'll include a variety of examples including classic games and a few applications."
Instructors: Matthew O. Jackson, Yoav Shoham.
Probabilistic Graphical Models (Computer Science)
"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. Topics include: (i) 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; (ii) reasoning and inference methods, including exact inference (variable elimination, clique trees) and approximate inference (belief propagation message passing, Markov chain Monte Carlo methods); (iii) learning methods for both parameters and structure in a PGM; (iv) using a PGM for decision making under uncertainty. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply PGM methods to computer vision, text understanding, medical decision making, speech recognition, and many other areas."
Instructor: Daphne Koller.
Cryptography (Computer Science)
"Cryptography is an indispensable tool for protecting information in computer systems. This course explains the inner workings of cryptographic primitives and how to correctly use them. Students will learn how to reason about the security of cryptographic constructions and how to apply this knowledge to real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two or more parties generate a shared secret key. We will cover the relevant number theory and discuss public-key encryption, digital signatures, and authentication protocols. Towards the end of the course we will cover more advanced topics such as zero-knowledge, distributed protocols such as secure auctions, and a number of privacy mechanisms. Throughout the course students will be exposed to many exciting open problems in the field."
Instructor: Dan Boneh.
Design and Analysis of Algorithms I (Computer Science)
"In this course you will learn several fundamental principles of algorithm design. You'll learn the divide-and-conquer design paradigm, with applications to fast sorting, searching, and multiplication. You'll learn several blazingly fast primitives for computing on graphs, such as how to compute connectivity information and shortest paths. Finally, we'll study how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures. Learn the answers to questions such as: How do data structures like heaps, hash tables, bloom filters, and balanced search trees actually work, anyway? How come QuickSort runs so fast? What can graph algorithms tell us about the structure of the Web and social networks? Did my 3rd-grade teacher explain only a suboptimal algorithm for multiplying two numbers?"
Instructor: Tim Roughgarden.
Computer Security (Computer Science)
"In this class you will learn how to design secure systems and write secure code. You will learn how to find vulnerabilities in code and how to design software systems that limit the impact of security vulnerabilities. We will focus on principles for building secure systems and give many real world examples. In addition, the course will cover topics such as:
- memory safety vulnerabilities,
- techniques and tools for vulnerability detection,
- sandboxing and isolation,
- web security,
- network security,
- malware detection and defense, and
- mobile platform security.
Instructors: Dan Boneh, John Mitchell, Dawn Song.
