In this session Carlo will give you an overview how Big Data is used in psychiatry and which Big Data systems are used for this discipline. After his talk you will understand machine learning algorithms for psychiatry AI application.
AutoML is becoming a pervasive tool for data scientists and machine learning practitioners to quickly build accurate machine learning models. Recent products from Google, Microsoft, Auger.AI and others emphasize a programmatic API approach (versus a visual leaderboard) to applying AutoML. All of these products have a similar processing pipeline to achieve a deployed prediction capability: data importing, configuring training, executing training, evaluating winning models, deploying a model for predictions, and reviewing on-going accuracy. With “AutoAutoM”L, ML practitioners can automatically retrain those models based on changing business conditions and discovery of new algorithms. But they are often practically locked into a single AutoML product due to the work necessary to program that particular AutoML product’s API. This talk describes an open source multivendor project called A2ML that allows developers to embed AutoML into their applications with any machine learning vendor’s product.
This talk will introduce pipe, an in-house Machine Learning Pipeline that has the capability to power thousands of ML models built on top of user data at Automattic, the company behind WordPress.com. A generalised machine learning pipeline, pipe serves the entire company and helps Automatticians seamlessly build and deploy machine learning models to predict the likelihood that a given event may occur, e.g., installing a plugin, purchasing a plan, or churning. The case studies will demonstrate how predictive models are used in marketing campaigns, discuss experiment setups for online evaluation, touch on uplift modeling, and most importantly, plot a journey to building models that are useful as opposed to just accurate.
With large data collected from assets, machine (supervised) learning is increasingly being used to develop predictive analytics solution for maintenance and failure prediction. Examples include failure prediction of main bearings in locomotive engines, troubleshooting of faults in healthcare assets like CT/MR scanners. For developing any supervised learning models, labeling of training data is imperative. In fact, error in labeling is the single biggest source of “bad” machine learning models. Typically, experts are consulted to label “interesting” events viz. failure of an asset, equipment downtime, part replacement etc. History of this “interesting” events resides in engineer/technician notes. The experts manually mine this notes and label the input data. In this presentation, Tapan will explain an unsupervised learning method which can mine through the technical notes and create a corpus of “interesting” events and label each data point to one of these events. It also allows for structured expert feedback to edit the labeling, if required.
If your stakeholders have tasked you with moving beyond science and experimentation into creating enterprise data products or analytical decision support tools that will show some business or user/customer value, then whether you’re a designer or not, there are UX research techniques you can apply today to increase your success. While machine learning, AI, and advanced analytics remain in the “hype” cycle, data teams still struggle to design engaging, valuable decision support tools that customers love. Why? Solutions are too often data-first and human-second, leaving customers scratching their head. In this interactive session, Brian will share some UX & design strategies that can help leaders create more engaging, useful, and usable analytics software that is rooted in what users and stakeholders actually need. He will end the ssession with some research interview practice (in pairs) so that participants can hopefully avoid creating one of the “80% of analytics insights that won’t deliver outcomes through 2022” (according to Gartner!)
In this session Yuri Katz will introduce a new method, based on the topological data analysis (TDA), to financial time series and detect early warning signals of approaching financial crashes. Analyzes of the time-series of daily log-returns of major US stock market indices and cryptocurrencies shows that in the vicinity of financial meltdowns, the Lp-norms of persistence topological landscapes exhibit strong growth. Remarkably, the average spectral density at low frequencies of the derived Lp-norms demonstrates a strong rising trend at least 100 trading days prior to either dotcom crash on 03/10/2000, or to the Lehman bankruptcy on 09/15/2008. The study suggests that TDA provides a new type of predictive analytics, which complements the standard statistical measures and ML-algorithms. The approach is very general and can be used beyond the analysis of financial time series.
With the advent of technology, the quality of the images has increased which in turn has increased the need for resources to process the images for building a model. The main question, however, is to discuss the need to develop lightweight models keeping the performance of the system intact. To connect the dots, in this session we will talk about the development of these applications specifically aimed to provide equally accurate results without using much of the resources and how this is achieved by using image processing techniques along with optimizing the network architecture.
Despite the massive quantities of behavioural data that E-commerce sites collect each day, the question persist: who are these online customers? Grasping the intentions and interests of online customer is one of the next steps for many e-commerce sites on their journey towards a data-driven culture. A website is a complex entity to be tamed, so how can we find a scalable approach to understand connections between pages, and how can we apply such insights for enhanced smart targeting strategies? In this talk, Benoit and Hassan offer you a glimpse into some approaches they have taken to solve this challenge, along with corresponding insights and challenges.