In this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest-neighbor methods, linear and logistic regressions, support vector machines and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely for clustering and dimensionality reduction.

Urbanization has a strong impact on the health and wellbeing of populations across the world. Predictive spatial modeling of urbanization therefore can be a useful tool for effective public health planning. Many spatial urbanization models have been developed using classic machine learning and numerical modeling techniques. However, deep learning with its proven capacity to capture complex spatiotemporal phenomena has not been applied to urbanization modeling. Here we explore the capacity of deep spatial learning for the predictive modeling of urbanization. We treat numerical geospatial data as images with pixels and channels, and enrich the dataset by augmentation, in order to leverage the high capacity of deep learning. Our resulting model can generate end-to-end multi-variable urbanization predictions, and outperforms a state-of-the-art classic machine learning urbanization model in preliminary comparisons.

A list of machine learning frameworks has come into the picture for the development and deployment of the AI apps. Developers are quite bewildered which framework to pick and which to ditch. Some of the frameworks would focus on the easy usability while others may put emphasis on the production deployment and parameter optimization. Every framework will have highs and lows of their own. They would have their own areas of excellence and downfalls making the choice for the developers even more difficult.

Several machine learning frameworks have emerged to streamline the development and deployment of AI applications. These frameworks help abstract away the grunt work of testing and configuring AI workloads for experimentation, optimization and production. However, developers need to make some hard choices when it comes to picking the right framework. Some may want to focus on ease of use when training a new AI algorithm, while others may prioritize parameter optimization and production deployment. Different frameworks have different strengths and weaknesses in these diverse areas.

An artificial neural network that's made entirely from DNA and mimics the way the brain works has been created by scientists in the lab. The test tube artificial intelligence can solve a classic machine learning problem by correctly identifying handwritten numbers. The work is a significant step in demonstrating the ability to program AI into man-made organic circuits, scientists claim. This could one day lead to human-like robots made from entirely organic materials, rather than the shiny metal cybermen seen in popular culture. Researchers hope the device will soon start forming its own'memories', from examples added to the test tube.

Deep learning's pre-eminence to the enterprise today is significant for two reasons. It represents the ultimate expression of machine learning's advanced capabilities and, as such, has become virtually synonymous with artificial intelligence because of its progressive learning prowess. Deep learning is at the core of the most intricate AI capabilities including speech recognition, image and video recognition, speech generation and aspects of robotics. In considering the massive influx of unstructured data besieging enterprises such as healthcare organizations, the ascending interest in AI, and the pivotal context with which deep learning purveys nearly any use case, it's clear 2018 is the year this technology's utility will finally supersede classic machine learning's. "Traditional machine learning is more like statistics," indico CEO Tom Wilde reflected.

In this special guest feature, Arvin Hsu, Senior Director of Data Science and Machine Learning for GoodData, discusses that despite the two terms being used interchangeably, deep learning and machine learning are very different in terms of the business problems they solve and the outcomes they enable. Arvin has over 15 years of experience in the field of Data Science and Data Modeling, including 6 years building Machine Learning based data products with both enterprise companies like Disney and startups. He's passionate about the innovations being created at the intersection of Big Data, Machine Learning, and Enterprise Data. He's also fascinated by how new technology will merge with ancient wisdoms to shift the way the world works. As artificial intelligence (AI) works its way into mainstream business practices, various different applications are coming up in conversations about how to best leverage the technology.

The topic of artificial intelligence (AI) is of great interest worldwide. As all industries undergo a digital transformation, they are finding that the ability to use data is both a competitive advantage and strategic imperative. Healthcare is certainly no exception. In this first part of a two-part blog series, I will discuss some of the technologies that make up artificial intelligence, and in the second post present five use cases for AI technology in healthcare. Within healthcare, as in many industries today, we are seeing a massive digital transformation taking place.