Standford is offering amazing online courses starting this Jan.Those who successfully complete the course will receive a statement of accomplishment. The courses are CS 101, Machine Learning, Software as a service, human-computer interaction, natural language processing, Game theory, Probablistic Graphical Models, Cryptography, Design and Analysis of Algorithm I.
To sign up, all you have to do is enter your name and email address and Stanford will send you the links to webinar and course materials when the course starts. We hope you make good use of your holidays during Jan – Feb and sign up for atleast 4 courses that you are interested in!
We have given a summary and video from the instructor in this post. Have a look at the following online courses. They would really be useful. Click Read more to find out the course details.
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.
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.
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.
In this course, you will learn how to design technologies that bring people joy, rather than frustration. You’ll learn several technique 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.
In many cases, we’ll use Web design as the anchoring domain. A lot of the examples will come from the Web, and we’ll talk just a bit about Web technologies in particular. When we do so, it will be to support the main goal of this course, which is helping you build human-centered design skills, so that you have the principles and methods to create excellent interfaces with any technology.
We are offering this course on Natural Language Processing free and online to students worldwide, January – March 2012,continuing Stanford’s exciting forays into large scale online instruction. 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. Taught by Professors Jurafsky and Manning, the curriculum draws from Stanford’s courses in Natural Language Processing. You will need a decent internet connection for accessing course materials, but should be able to watch the videos on your
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.
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 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?