Free Online IT/CS Courses in Stanford University for 2012

A major research university in computer science, mathematics, natural sciences, and social sciences, Stanford University in California is offering free online courses for next year, 2012 and you can already register today for a reservation. The university will bring distance-learning expertise to the table for both SCPD students and regular Stanford students, as well as the general public. Students will have access to the new online learning tools and are expected to read course materials, complete assignments and take quizzes and an exam.

Here are the list of free IT/Computer Science courses from Stanford University:

Course: Design and Analysis of Algorithms I

Instructor: Tim Roughgarden

Class Starts: January 23, 2012

Short Course Description: 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?

Prerequisites: How to program in at least one programming language (like C, Java, or Python); familiarity with proofs, including proofs by induction and by contradiction; and some discrete probability, like how to compute the probability that a poker hand is a full house. At Stanford, a version of this course is taken by sophomore, junior, and senior-level computer science majors.

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Course: Cryptography

Instructor: Professor Dan Boneh

Class Starts: January 2012 

Short Course Description: 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.

The course will include written homeworks and programming labs. The course is self-contained, however it will be helpful to have a basic understanding of discrete probability theory.

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Course: Natural Language Processing

Instructor: Dan Jurafsky and Christopher Manning

Class Starts: January 23, 2012

Short Course Description: The course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.

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Course: Human-Computer Interaction

Instructor: Scott Klemmer

Class Starts: January 2012

Short Course Description: 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. 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.

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Course: Probabilistic Graphical Models

Instructor: Professor Daphne Koller

Class Starts: January 2012

Short Course Description: 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.

  

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Course: Computer Science 101

Instructor: Nick Parlante

Class Starts: February 2012

Short Course Description: 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.

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Course: Software Engineering for Software as a Service

Instructor: Armando Fox and David Patterson

Class Starts: February 2012

Short Course Description: 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.

Prerequisites: Programming proficiency in an object-oriented programming language such as Java, C#, C++, Python, or Ruby. Basic Unix command-line skills are helpful; we will provide a cheat sheet. You must also have a computer running Windows, Mac OS, Linux, or Solaris operating systems and running x86 or AMD64/Intel64 hardware on which you can install and run VirtualBox virtual machine. It should have at least 512 MB of memory, or at least 1 GB if running Windows. See www.virtualbox.org.

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Course: Game Theory

Instructor: Matthew O. Jackson and Yoav Shoham

Class Starts: February 2012

Short Course Description: 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. 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.