This course covers the basic data science skills of python and text mining of keywords. The student will also learn simple search and recommendation algorithms. Data processing, calculations, and analysis related to keyword extraction will be taught using a hands-on project / coding test based approach. Python will be taught in a systematic, example based method using the text dataset included especially for this course. In addition to python, the exercises will include application of skills using the emacs editor.

The increasing sophistication of automated systems will have far-reaching implications for work and employment, and governments should be ready for upheaval. "WHO IS READY FOR THE COMING WAVE OF AUTOMATION? The Automation Readiness Index", created by The Economist Intelligence Unit and sponsored by ABB, assesses how well-prepared 25 countries are for the challenges and opportunities of intelligent automation. The Automation Readiness Index compares countries on their preparedness for the age of intelligent automation. In assessing the existence of policy and strategy in the areas of innovation, education and the labour market, the study finds that little policy is in place today that specifically addresses the challenges of AI- and robotics-based automation.

Recent research has shown that deep ensemble for forest can achieve a huge increase in classification accuracy compared with the general ensemble learning method. Especially when there are only few training data. In this paper, we decide to take full advantage of this observation and introduce the Dense Adaptive Cascade Forest (daForest), which has better performance than the original one named Cascade Forest. And it is particularly noteworthy that daForest has a powerful ability to handle high-dimensional sparse data without any preprocessing on raw data like PCA or any other dimensional reduction methods. Our model is distinguished by three major features: the first feature is the combination of the SAMME.R boosting algorithm in the model, boosting gives the model the ability to continuously improve as the number of layer increases, which is not possible in stacking model or plain cascade forest. The second feature is our model connects each layer to its subsequent layers in a feed-forward fashion, to some extent this structure enhances the ability of the model to resist degeneration. When number of layers goes up, accuracy of model goes up a little in the first few layers then drop down quickly, we call this phenomenon degeneration in training stacking model. The third feature is that we add a hyper-parameter optimization layer before the first classification layer in the proposed deep model, which can search for the optimal hyper-parameter and set up the model in a brief period and nearly halve the training time without having too much impact on the final performance. Experimental results show that daForest performs particularly well on both high-dimensional low-order features and low-dimensional high-order features, and in some cases, even better than neural networks and achieves state-of-the-art results.

For developers, deep learning systems are becoming more interactive and complex. From the building of more malleable datasets that can be iteratively augmented, to more dynamic models, to more continuous learning being built into neural networks, there is a greater need to manage the process from start to finish with lightweight tools. "New training samples, human insights, and operation experiences can consistently emerge even after deployment. The ability of updating a model and tracking its changes thus becomes necessary," says a team from Imperial College London that has developed a library to manage the iterations deep learning developers make across complex projects. "Developers have to spend massive development cycles on integrating components for building neural networks, managing model lifecycles, organizing data, and adjusting system parallelism."

Like many data scientists, I desired to contribute to open source, but I thought that "open source contribution" meant creating a new library in Python. That would require expertise in objects, inheritance, parallelism, asynchronous, classes, methods, decorators, and more to write that long, complex code. But, I'm a statistician and that level of Python/computer science is beyond my scope of knowledge. In early 2017, Andreas Mueller, who is the core maintainer of the Python analysis library scikit-learn and co-author of Introduction to Machine Learning with Python reached out to me, as an organizer for the NYC Women in Machine Learning & Data Science meetup group, to increase the participation of women in open source. A 2013 survey found that only 11 percent of open-source contributors were women. There is more background in this article: And Now, an Infuriating Statistic about Women and Coding.

Reading comprehension is one of the fundamental skills for human, which one must learn systematically since the elementary school. Do you still remember how the worksheet of your reading class looks like? It usually consists of an article and few questions about its content. To answer these questions, you need to first gather information by collecting answer-related sentences from the article. Sometimes you can directly copy those original sentences from the article as the final answer.

Just when we think we have a handle on all the incredible ways that dogs enhance our lives and our understanding of the world, new work with dogs expands that sphere even further. Graduate student Kiana Ehsani at the University of Washington has a great collaborator named Kelp, an Alaskan Malamute, who is a key partner in her quest to create an artificial intelligence system that thinks like a dog. The long-term goal is to produce a robot that is enough like a dog to perform many of the task that dogs are trained to do for humans. Though that may seem like a faraway dream, Ehsani's research project is edging ever closer to that possibility. Generally, the goal of the current research was to study and emulate the dog's response to visual information.

Parents worry about a lot of things--like whether their children will get into college, or become drug addicts, or get abducted by strangers. But I spend a lot more time worrying that my children are going to live with us forever because robots have taken all their potential jobs. As somebody who has spent her adult life focused largely on two things--studying technology trends and raising children--I'm acutely aware of the effect that continued advances in artificial intelligence could have on my children's opportunities. After all, a recent McKinsey report predicts that by 2030, when my two children are just joining the workforce, up to 30% of today's current work will have been automated. The problem is, we don't know for certain which particular jobs will be automated years from now, because AI is constantly developing in surprising ways.

Do you know key roles for AI in learning? Artificial intelligence plays a big role in learning because schools became more technologically advanced and require new teaching methods to engage young people. AI may ask questions from the reading and add supplementary questions about the main idea to make you answer correctly. The system was developed to strengthen and personalize the education to each learner and provide additional information when they confused. The programs may teach learners basic things to move on and answer their questions instantly, provide regular feedback.