The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions.

This Scala training course from Infinite Skills teaches you everything you need to know about the Scala programming language. This course is designed for users that already have some programming experience. You will start by learning the language basics of Scala, including sequences, recursion, and nesting functions. The course will then teach you about the object-oriented aspects of Scala, linearization of trait methods, and building and writing with XML. This video tutorial also covers text processing, parallelism and actors, and libraries for unit testing.

We describe a data-driven discovery method that leverages Simpson's paradox to uncover interesting patterns in behavioral data. Our method systematically disaggregates data to identify subgroups within a population whose behavior deviates significantly from the rest of the population. Given an outcome of interest and a set of covariates, the method follows three steps. First, it disaggregates data into subgroups, by conditioning on a particular covariate, so as minimize the variation of the outcome within the subgroups. Next, it models the outcome as a linear function of another covariate, both in the subgroups and in the aggregate data. Finally, it compares trends to identify disaggregations that produce subgroups with different behaviors from the aggregate. We illustrate the method by applying it to three real-world behavioral datasets, including Q\&A site Stack Exchange and online learning platforms Khan Academy and Duolingo.

Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of peptide identification, the challenge remains on large-scale datasets and datasets with an extremely large proportion of false positives (hard datasets). A more efficient learning strategy is required for improving the performance of peptide identification on challenging datasets. Results: In this work, we present an online learning method to conquer the challenges remained for exiting peptide identification algorithms. We propose a cost-sensitive learning model by using different loss functions for decoy and target PSMs respectively. A larger penalty for wrongly selecting decoy PSMs than that for target PSMs, and thus the new model can reduce its false discovery rate on hard datasets. Also, we design an online learning algorithm, OLCS-Ranker, to solve the proposed learning model. Rather than taking all training data samples all at once, OLCS-Ranker iteratively feeds in only one training sample into the learning model at each round. As a result, the memory requirement is significantly reduced for large-scale problems. Experimental studies show that OLCS-Ranker outperforms benchmark methods, such as CRanker and Batch-CS-Ranker, in terms of accuracy and stability. Furthermore, OLCS-Ranker is 15--85 times faster than CRanker method on large datasets. Availability and implementation: OLCS-Ranker software is available at no charge for non-commercial use at https://github.com/Isaac-QiXing/CRanker.

While tree methods have been popular in practice, researchers and practitioners are also looking for simple algorithms which can reach similar accuracy of trees. In 2010, (Ping Li UAI'10) developed the method of "abc-robust-logitboost" and compared it with other supervised learning methods on datasets used by the deep learning literature. In this study, we propose a series of "tunable GMM kernels" which are simple and perform largely comparably to tree methods on the same datasets. Note that "abc-robust-logitboost" substantially improved the original "GDBT" in that (a) it developed a tree-split formula based on second-order information of the derivatives of the loss function; (b) it developed a new set of derivatives for multi-class classification formulation. In the prior study in 2017, the "generalized min-max" (GMM) kernel was shown to have good performance compared to the "radial-basis function" (RBF) kernel. However, as demonstrated in this paper, the original GMM kernel is often not as competitive as tree methods on the datasets used in the deep learning literature. Since the original GMM kernel has no parameters, we propose tunable GMM kernels by adding tuning parameters in various ways. Three basic (i.e., with only one parameter) GMM kernels are the "$e$GMM kernel", "$p$GMM kernel", and "$\gamma$GMM kernel", respectively. Extensive experiments show that they are able to produce good results for a large number of classification tasks. Furthermore, the basic kernels can be combined to boost the performance.

China was late to the last industrial revolution, but with the arrival of AI it is determined not to miss the next one. The Chinese government is introducing a textbook called "Fundamentals of Artificial Intelligence" to 40 high schools, according to the South China Morning Post. It describes the history of AI and how the technology can be applied in areas like facial recognition, autonomous driving, and public security. Last year, the central government asked the country's education policy makers to include AI courses in primary and secondary schools. The nine-chapter book was penned by an all-star lineup, including the chairman of one of the world's most valuable AI startups, SenseTime.

May 7, 2018 --A streak of rocket fire pierced the foggy predawn skies of southern California Saturday, as NASA sent off its latest Mars mission. The InSight mission is set to rack up a series of "firsts." It will also be the first time CubeSats will deploy in deep space. And, if the mission is successful, it will be the first time that scientists gather direct data on the interior of another planet and detect quakes on another planet. Despite all these firsts, the mission marks the 45th time humans have sent robotic envoys to uncover Mars's secrets (although only about half of those missions are considered a success).

This amazing liveVideo course will put your machine learning on the fast track! AWS Machine Learning in Motion gives you a complete tour of the essential tools, techniques, and concepts you need to do complex predictions and other data analysis using the AWS machine learning services! In this interactive liveVideo course, you'll get started with cloud-based machine learning under the guidance of experienced software engineer and TED Speaker Kesha Williams. You'll cut through the theory and jargon as you build a working crime-fighting machine learning algorithm! Starting with a tour of AWS' tools and the basics of machine learning, you'll dive into the learning algorithms supported by AWS, such as linear regression, multinomial logistic regression, and logistic regression.

Artificial Intelligence has been in the news a lot lately, from ominous warnings of its future implications from academic leaders like Stephen Hawking and Elon Musk, to panic around Facebook AI developing its own language. And according to a recent report from the McKinsey Global Institute, roughly half of today's work activities could be automated by 2055. Could "teaching" be on that list? Today, Education World examines the current nature of AI's role in academia, including a prediction of where we're likely headed. It's not as scary as you think!

Artificial Intelligence has been in the news a lot lately, from ominous warnings of its future implications from academic leaders like Stephen Hawking and Elon Musk, to panic around Facebook AI developing its own language. And according to a recent report from the McKinsey Global Institute, roughly half of today's work activities could be automated by 2055. Could "teaching" be on that list? Today, Education World examines the current nature of AI's role in academia, including a prediction of where we're likely headed. It's not as scary as you think!