Monday, August 20, 2012

Stanford’s Free Online Courses

Stanford Online Courses

stanford.edu | Nov 30th -0001

Stanford currently offers eleven free online courses from the School of Engineering. These online classes are taught by regular Stanford faculty and are highly interactive. Enrollees do not get Stanford credit for their work, but they do receive a statement of accomplishment if they successfully complete a course. The classes are delivered via the Coursera platform.

Current courses include:

Begins April 23

Automata

Jeffrey Ullman, Professor
This course covers finite automata, context-free grammars, Turing machines, undecidable problems, and intractable problems (NP-completeness).
Begins April 23

Compilers

Alex Aiken, Professor
This course will discuss the major ideas used today in the implementation of programming language compilers. You will learn how a program written in a high-level language designed for humans is systematically translated into a program written in low-level assembly more suited to machines!
Begins April 23

Computer Science 101

Nick Parlante
CS101 teaches the essential ideas of Computer Science for a zero-prior-experience audience. The course uses small coding experiments in the browser to play with the nature of computers, understanding their strengths and limitations.
Begins April 23

Introduction to Logic

Michael Genesereth, Professor
In this course, you will learn how to formalize information and reason systematically to produce logical conclusions. We will also examine logic technology and its applications - in mathematics, science, engineering, business, law, and so forth.
Begins April 23

Machine Learning

Andrew Ng, Associate Professor
Learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.
Design and Analysis of Algorithms

Associate Professor Tim Roughgarden
In this course you'll learn several fundamental principles of algorithm design. The course will cover the divide-and-conquer design paradigm, several blazingly fast primitives for computing on graphs, and how allowing the computer to "flip coins" can lead to elegant and practical algorithms and data structures.
Natural Language Processing

Professor Dan Jurafsky and Associate Professor Christopher Manning
Natural language processing is the technology for dealing with our most ubiquitous product: human language. 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.
Cryptography

Professor Dan Boneh
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.
Game Theory

Professors Matthew O. Jackson and Yoav Shoham
Game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents. 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.
Probabilistic Graphical Models

Professor Daphne Koller
Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world. In this class, you'll 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 and make good decisions under uncertainty.