In this work, we propose an automated method to identify semantic bugs in student programs, called ATAS, which builds upon the recent advances in both symbolic execution and active learning. Symbolic execution is a program analysis technique which can generate test cases through symbolic constraint solving. Our method makes use of a reference implementation of the task as its sole input. We compare our method with a symbolic execution-based baseline on 6 programming tasks retrieved from CodeForces comprising a total of 23K student submissions. We show an average improvement of over 2.5x over the baseline in terms of runtime (thus making it more suitable for online evaluation), without a significant degradation in evaluation accuracy.

With this course, you'll have the keys to the entire R Machine Learning Kingdom!

MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. This video will help beginners build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and you'll see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll learn about the different types of regression technique and how to apply them to your data using the MATLAB functions.

Artificial intelligence will transform many companies and create completely new types of businesses. Andrew Ng, cofounder of Coursera, AI Fund, and Landing.AI and Google Brain, shares how businesses can benefit. The interview was conducted by Michael Chui, a partner of the McKinsey Global Institute. I think it clarifies some interesting issues. Artificial intelligence (AI) is at the cutting edge of innovation.

Learn how to select features and build simpler, faster and more reliable machine learning models. This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor's experience as a Data Scientist. You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set. The course starts describing simple and fast methods to quickly screen the data set and remove redundant and irrelevant features.

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems. In this course, we will start from the very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code.

Machine Learning is everywhere these days. We live in a world where Machine Learning and Artificial Intelligence is not obscure mathematical and science fiction anymore they have become crucial part of our lives. Netflix, Amazon, Siri, Pandora, Google, Prisma the list goes on and on and it's not just entertainment and media, It's even the post office to healthcare and traffic to security. Close analysis suggests that virtually every moment of our lives we are touched by Machine Learning at some point. With so much innovation going on in Machine Learning field and how it has improved the way of life who wouldn't wanna be part of it in this amazing time.

In statistics, Linear Regression is a linear approach for modeling the relationship between a scalar dependent variable Y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression. In Linear Regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models.

This volume introduces working with text, with a focus on the most plentiful source of text out there: email. Working with email text from your own Gmail account, you will build up a label predictor, similar in effect to the technology Google uses to power the Social and Promotions tabs. With this technique, you will be able to build your own email classification and automated workflow hooks. Will Ballard serves as Chief Technology Officer at GLG and is responsible for the Engineering and IT organizations. Prior to joining GLG, Will was the Executive Vice President of Technology and Engineering at Demand Media.

Microsoft is the latest company to offer courses in artificial intelligence (A.I.). Microsoft's Professional Program for Artificial Intelligence includes 10 online courses that teach 10 skills. Each course takes roughly 8-16 hours to complete and kicks off at the beginning of each quarter. The courses, which can be taken in any order, range from "Introduction to AI" to "Essential Mathematics for Artificial Intelligence." The artificial-intelligence program joins a number of other Microsoft professional tracks, including software development, IT support, DevOps, and more.