For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data.

This summer, Blakeley Payne, a graduate student at MIT, ran a week-long course on ethics in artificial intelligence for 10-14 year olds. In one exercise, she asked the group what they thought YouTube's recommendation algorithm was used for. "To get us to see more ads," one student replied. "These kids know way more than we give them credit for," Payne said. Payne created an open source, middle-school AI ethics curriculum to make kids aware of how AI systems mediate their everyday lives, from YouTube and Amazon's Alexa to Google search and social media.

The artificial intelligence system, called Aristo, just passed an eighth-grade science test, a benchmark in AI development that scientists had been aiming to reach for four years, The New York Times reports. It got an A on the quiz, correctly answering more than 90% of the questions on the test designed for New York students, and then it went on to answer questions on an exam for 12th graders, earning a solid B (80%). The AI's science prowess shows how far artificial intelligence has come at mimicking human logic, language, and decision-making. Unlike your average eighth grader, the AI, which was designed by the Allen Institute--the lab founded by the late Microsoft cofounder Paul Allen--was built solely to take multiple-choice tests. According to the Times, the researchers view standardized science tests as a more meaningful AI benchmark than the machine's ability to play chess, and Aristo passed with flying colors.

Paul Hildreth looked at images from security cameras set up at schools in Fulton County, Georgia. He began watching a video of a woman walking inside one of the school buildings. The top of her clothing was bright yellow. Hildreth used his computer's artificial intelligence, or AI system to find other images of the woman. The system put the pictures together in a video that showed where she currently was, where she had been and what she was doing.

The training and development of your workforce is vital to the achievement of digital transformation success for businesses. And today, more and more businesses are leveraging e-learning to educate their employees. The advantages for businesses using online learning platforms as opposed to traditional training methods are bountiful. First, it lowers business costs since one training session can be delivered to multiple people. Second, topics can be broken down into bite-sized chunks, meaning that employees do not need to spend lengthy periods of time away from their desks.

'For species like chimpanzees, which have complex social lives and live for many years, getting snapshots of their behaviour from short-term field research can only tell us so much,' says Dan Schofield, researcher and DPhil student at Oxford University's Primate Models Lab, School of Anthropology. 'By harnessing the power of machine learning to unlock large video archives, it makes it feasible to measure behaviour over the long term, for example observing how the social interactions of a group change over several generations.' The computer model was trained using over 10 million images from Kyoto University's Primate Research Institute (PRI) video archive of wild chimpanzees in Guinea, West Africa. The new software is the first to continuously track and recognise individuals in a wide range of poses, performing with high accuracy in difficult conditions such as low lighting, poor image quality and motion blur. 'Access to this large video archive has allowed us to use cutting edge deep neural networks to train models at a scale that was previously not possible,' says Arsha Nagrani, co-author of the study and DPhil student at the Department of Engineering Science, University of Oxford.

The goal of data science teams are to build and deploy high impact models. Data scientists prefer to focus on building algorithms, while data engineers focus on performance and productionizing machine learning. Kubernetes is an orchestration platform that can be deployed anywhere and can serve any kind of machine and deep learning environment. Kubernetes is a great tool for data scientists to use to stay productive and for data engineers to get production-ready results. In this free workshop you'll learn how to build your own Kubernetes to use in your next machine learning pipeline.

-- The increasing inclusion of Machine Learning (ML) models in safety critical systems like autonomous cars have led to the development of multiple model-based ML testing techniques. One common denominator of these testing techniques is their assumption that training programs are adequate and bug-free. These techniques only focus on assessing the performance of the constructed model using manually labeled data or automatically generated data. However, their assumptions about the training program are not always true as training programs can contain inconsistencies and bugs. In this paper, we examine training issues in ML programs and propose a catalog of verification routines that can be used to detect the identified issues, automatically. We implemented the routines in a T ensorflow-based library named TFCheck. Using TFCheck, practitioners can detect the aforementioned issues automatically. T o assess the effectiveness of TFCheck, we conducted a case study with real-world, mutants, and synthetic training programs. Results show that TFCheck can successfully detect training issues in ML code implementations. I. INTRODUCTION Nowadays, software applications powered by Machine Learning (ML) are increasingly being deployed in safety-critical systems such as self-driving cars or aircraft collision-avoidance systems. Therefore, their reliability is now of paramount importance. Recently, researchers have proposed many testing approaches to help improve the reliability of ML applications [1].

Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simplify the operations for compute acceleration. Recently, researches on DNN quantization develop from inference to training, laying a foundation for the online training on accelerators. However, existing schemes leaving batch normalization (BN) untouched during training are mostly incomplete quantization that still adopts high precision FP in some parts of the data paths. Currently, there is no solution that can use only low bit-width INT data during the whole training process of large-scale DNNs with acceptable accuracy. In this work, through decomposing all the computation steps in DNNs and fusing three special quantization functions to satisfy the different precision requirements, we propose a unified complete quantization framework termed as "WAGEUBN" to quantize DNNs involving all data paths including W (Weights), A (Activation), G (Gradient), E (Error), U (Update), and BN. Moreover, the Momentum optimizer is also quantized to realize a completely quantized framework. Experiments on ResNet18/34/50 models demonstrate that WAGEUBN can achieve competitive accuracy on ImageNet dataset. For the first time, the study of quantization in large-scale DNNs is advanced to the full 8-bit INT level. In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency. Our throughout quantization framework has great potential for future efficient portable devices with online learning ability.

An artificial intelligence system has made history by being the first to pass an eighth grade science test with flying colors. According to The New York Times, researchers at the Allen Institute for Artificial Intelligence in Seattle Washington have cracked the code for test-taking computers. Its system, called Aristo, received a 90 percent score on an eighth-grade science test and passed with an 80 percent grade on a 12th-grade exam. An AI passed an eighth grade science exam with flying colors, marking a first for the technology. Scientists four years ago failed to get AI to achieve a passing grade.