Lee Kai-Fu has always been very bullish about the future of artificial intelligence (AI) in China. He started off his keynote speech at an AI conference at the Massachusetts Institute of Technology in November by predicting that self-driving cars will become a mass phenomenon in the U.S. in 15 to 20 years. But in China, he said, it will take "more like 10 years." "Although there are concerns about whether there is an emerging AI bubble in China, I'd say there isn't one," he told Caixin. Lee is a real insider when it comes to assessing the state of AI development in both North America and China. He completed his doctorate in computer-aided speech recognition at Carnegie-Mellon University (CMU) in 1988 and went on to work at Apple Inc., Silicon Graphics Inc. and Microsoft Corp., and head Google Inc.'s China business.

I was recently a guest lecturer at the University of California Berkeley Extension in San Francisco. On a lovely Saturday afternoon, the classroom was crowded with students of all ages learning the tools of the modern economy. The craftspeople of the "Analytics Revolution" were busy learning new skills and tools that will prepare them for this Brave New World of analytics. I was blown away by their dedication! As we teach the next generation, it's important that we focus more on capabilities and less so on skills.

This is a "hands-on" business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today. The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on "best practices" for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer's hard drive. All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can "take them home" and apply them to their own unique data analysis and mining cases.

The IEEE publishes an annual list of the Top 10 Technology Trends for each upcoming year. Making the list for 2018 are multiple topics surrounding artificial intelligence and machine learning. Deep learning comes in as the IEEE hottest trend for 2018. Neural networks extract features through a concept of layers. By combining the output from these multiple layers, deeper layers are able to construct more advanced insight from data.

About this course: This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models.

This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i.e. a marker. In this paper, we describe the factorial marked point processes whereby time-stamped event is factored into multiple markers. Accordingly the size of the infectivity matrix modeling the effect between pairwise markers is in power order w.r.t. the number of the discrete marker space. We propose a decoupled learning method with two learning procedures: i) directly solving the model based on two techniques: Alternating Direction Method of Multipliers and Fast Iterative Shrinkage-Thresholding Algorithm; ii) involving a reformulation that transforms the original problem into a Logistic Regression model for more efficient learning. Moreover, a sparse group regularizer is added to identify the key profile features and event labels. Empirical results on real world datasets demonstrate the efficiency of our decoupled and reformulated method. The source code is available online.

Deep learning has recently seen rapid development and significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the innate complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such high performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, noting its short yet impactful history and summarize the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where), to thoroughly summarize deep learning visual analytics research. We conclude by highlighting research directions and open research problems. This survey helps new researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.

R is an open source programming language and software environment for statistical computing and graphics. R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data like optimization and analyzation are cleared with R's excellent data visualization feature.

Decision Tree Model building is one of the most applied technique in analytics vertical. The decision tree model is quick to develop and easy to understand. The technique is simple to learn. A number of business scenarios in lending business / telecom / automobile etc. require decision tree model building. How long the course should take?

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition. I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners -- we start off with the fundamentals of neural networks and machine learning and by the end of the program you're training state-of-the-art networks on the ImageNet dataset from scratch. Along the way I quickly realized that a stumbling block for many readers is configuring their development environment -- especially true for those wanted to utilize their GPU(s) and train deep neural networks on massive image datasets (such as ImageNet).