Coursera

Facebook Twitter
Class Central - Google+ Class Central - Google+ New +Udacity course - Intro to Object Oriented Programming. Starts April 23rd. DescriptionIn this introductory programming class, you’ll learn Object Oriented Programming, a must-have technique for software engineers that will allow you to reuse and share code easily. You’ll learn by doing, and will build byte-sized (ha!) mini projects in each lesson to learn and practice programming concepts.
Computer Vision loading What is Computer Vision? Computer vision seeks to develop algorithms that replicate one of the most amazing capabilities of the human brain - inferring properties of the external world purely by means of the light reflected from various objects to the eyes. We can determine how far away these objects are, how they are oriented with respect to us, and in relationship to various other objects. We reliably guess their colors and textures, and we can recognize them - this is a chair, this is my dog Fido, this is a picture of Bill Clinton smiling. Computer Vision
Information Theory Information Theory About The Course 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.
Machine Learning

Machine Learning

loading What Is Machine Learning? Machine learning is the science of getting computers to act without being explicitly programmed.
Computer Security The Course 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. Computer Security

Game Theory

loading About the Course 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? Game Theory
What are Probabilistic Graphical Models? 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.

Probabilistic Graphical Models

Probabilistic Graphical Models
Software Engineering for Software as a Service

Software Engineering for Software as a Service

loading About The Course This course teaches fundamental processes of software engineering using the highly-productive Agile development method for Software as a Service (SaaS) using Ruby on Rails. This is not a "web programming" course: the emphasis is on learning the processes, tools and concepts, using SaaS as the vehicle. We chose SaaS and Rails because we believe the best tools for teaching these concepts are those in the Rails ecosystem.
About The Course This 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. We are offering this course on Natural Language Processing free and online to students worldwide, 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.

Natural Language Processing

Natural Language Processing