Free Course - Data Analytics Learning Path 2020 A comprehensive way to master Data Analytics now at the tip of your fingers. This path includes hand-picked courses which will help you master Data Analytics with ease. It include courses on Microsoft Excel, SQL for Data Analytics, building regression models in Python, and classification models in Python. Instructor driven project Enroll Now - Data Analytics Learning Path Microsoft Excel Masterclass: Complete and Concise Course Instructor: Start Tech Academy This online tutorial teaches you complete MS Excel from the scratch covering all the essential topics such as Pivots, Macros and Analytics. Enroll now! 3 Hours 17 Lectures 2 SQL Masterclass: SQL for Data Analytics Course Instructor: Start Tech Academy Learning SQL for Data Analytics is now easy with this online tutorial.

Apple's Face ID does not recognize users wearing a surgical mask, forcing them to type in their passcode or remove the protective gear to unlock the smartphone. Researchers have now shared a hack that trains the technology to accept your face with or without a mask. The video tutorial shows a user folding a brand-new mask in half, laying it on one side of their face and going through the process of setting up the biometric system. Users can either'Reset Face ID' or choose to make their masked face an'Alternate Appearance.' Researchers have now shared a trick that train the technology, which involves snapping pictures of your face with just half of it covered by a mask. The Centers for Disease Control (CDC) has recommended that Americans consider wearing masks to limit the spread of the coronavirus that is sweeping the nation.

Dr. Qin will introduce ...from the Abcus to Artificial Intelligence of history and share the ideas and strategies for the future of Artificial Intelligence and computer science learning. Please join us on our Facebook page for an interactive CS lesson!

Have you heard that SAS offers a collection of new, high-performance CAS procedures that are compatible with a multi-threaded approach? The free e-book Exploring SAS Viya: Data Mining and Machine Learning is a great resource to learn more about these procedures and the features of SAS Visual Data Mining and Machine Learning. Download it today and keep reading for an excerpt from this free e-book! In SAS Studio, you can access tasks that help automate your programming so that you do not have to manually write your code. In this blog post, you will learn the syntax for two of the new, advanced data mining and machine learning procedures: PROC TEXTMINE and PROCTMSCORE.

In light of the recent events surrounding Covid-19, learning for grades K-12 looks very different than it did a month ago. Parents and educators may be feeling overwhelmed about turning their homes into classrooms. With that in mind, a team led by Media Lab Associate Professor Cynthia Breazeal has launched aieducation.mit.edu to share a variety of online activities for K-12 students to learn about artificial intelligence, with a focus on how to design and use it responsibly. Learning resources provided on this website can help to address the needs of the millions of children, parents, and educators worldwide who are staying at home due to school closures caused by Covid-19, and are looking for free educational activities that support project-based STEM learning in an exciting and innovative area. The website is a collaboration between the Media Lab, MIT Stephen A. Schwarzman College of Computing, and MIT Open Learning, serving as a hub to highlight diverse work by faculty, staff, and students across the MIT community at the intersection of AI, learning, and education. "MIT is the birthplace of Constructionism under Seymour Papert.

In the contemporary big data realm, Deep Neural Networks (DNNs) are evolving towards more complex architectures to achieve higher inference accuracy. Model compression techniques can be leveraged to efficiently deploy such compute-intensive architectures on resource-limited mobile devices. Such methods comprise various hyper-parameters that require per-layer customization to ensure high accuracy. Choosing such hyper-parameters is cumbersome as the pertinent search space grows exponentially with model layers. This paper introduces GeneCAI, a novel optimization method that automatically learns how to tune per-layer compression hyper-parameters. We devise a bijective translation scheme that encodes compressed DNNs to the genotype space. The optimality of each genotype is measured using a multi-objective score based on accuracy and number of floating point operations. We develop customized genetic operations to iteratively evolve the non-dominated solutions towards the optimal Pareto front, thus, capturing the optimal trade-off between model accuracy and complexity. GeneCAI optimization method is highly scalable and can achieve a near-linear performance boost on distributed multi-GPU platforms. Our extensive evaluations demonstrate that GeneCAI outperforms existing rule-based and reinforcement learning methods in DNN compression by finding models that lie on a better accuracy-complexity Pareto curve.

The information bottleneck (IB) principle offers both a mechanism to explain how deep neural networks train and generalize, as well as a regularized objective with which to train models. However, multiple competing objectives have been proposed based on this principle, and the information-theoretic quantities in these objectives are difficult to compute for large deep neural networks. This, in turn, limits their use as a training objective. In this work, we review these quantities, compare and unify previously proposed objectives and relate them to surrogate objectives more friendly to optimization. We find that these surrogate objectives allow us to apply the information bottleneck to modern neural network architectures. We demonstrate our insights on Permutation-MNIST, MNIST and CIFAR10.

Ordinal peer grading has been proposed as a simple and scalable solution for computing reliable information about student performance in massive open online courses. The idea is to outsource the grading task to the students themselves as follows. After the end of an exam, each student is asked to rank -- in terms of quality -- a bundle of exam papers by fellow students. An aggregation rule then combines the individual rankings into a global one that contains all students. We define a broad class of simple aggregation rules, which we call type-ordering aggregation rules, and present a theoretical framework for assessing their effectiveness. When statistical information about the grading behaviour of students is available (in terms of a noise matrix that characterizes the grading behaviour of the average student from a student population), the framework can be used to compute the optimal rule from this class with respect to a series of performance objectives that compare the ranking returned by the aggregation rule to the underlying ground truth ranking. For example, a natural rule known as Borda is proved to be optimal when students grade correctly. In addition, we present extensive simulations that validate our theory and prove it to be extremely accurate in predicting the performance of aggregation rules even when only rough information about grading behaviour (i.e., an approximation of the noise matrix) is available. Both in the application of our theoretical framework and in our simulations, we exploit data about grading behaviour of students that have been extracted from two field experiments in the University of Patras.

Upper Confidence Bound) You will know how to evaluate your model, what underfitting and overfitting is, why resampling techniques are important, and how you can split your dataset into parts (train/validation/test). We will understand the theory behind deep neural networks. We will understand and implement convolutional neural networks - the most powerful technique for image recognition. Description Did you ever wonder how machines "learn" - in this course you will find out. We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ... For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions.

Sign in to report inappropriate content. Michael Littman is a professor of computer science at Brown University. He is an AAAI Fellow and an ACM Fellow. He's been contributing to the field of AI and Reinforcement Learning since the early days. He's helped a new generation of RL students by creating the Machine Learning and the Reinforcement Learning and Decision Making lectures available online on Udacity for free, which is the same course material used at Georgia Tech's OMSCS program.