�ݺ�ߣshows by User: africaperianez

�ݺ�ߣshows by User: africaperianez / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: africaperianez / Wed, 10 Apr 2019 06:57:38 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: africaperianez Data Assimilation in Numerical Weather Prediction Models /slideshow/data-assimilation-in-numerical-weather-prediction-models/140288872 dacern-190410065738
Data Assimilation in Numerical Weather Prediction Models]]>
Data Assimilation in Numerical Weather Prediction Models]]> Wed, 10 Apr 2019 06:57:38 GMT /slideshow/data-assimilation-in-numerical-weather-prediction-models/140288872 africaperianez@slideshare.net(africaperianez) Data Assimilation in Numerical Weather Prediction Models africaperianez Data Assimilation in Numerical Weather Prediction Models <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dacern-190410065738-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data Assimilation in Numerical Weather Prediction Models

Data Assimilation in Numerical Weather Prediction Models from Africa Perianez

]]> 504 1 https://cdn.slidesharecdn.com/ss_thumbnails/dacern-190410065738-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Custom-Made Games with Machine Learning and Big Data /slideshow/custommade-games-with-machine-learning-and-big-data/139220357 ydperianezgdc2019-190402094346
Abstract Video games define our era. Players generate extremely rich behavioral datasets that constitute an ideal playground to understand human behavior. With this unique source of information, machine learning can not only explore social and consumer dynamics, but also predict users' personalities. Yokozuna Data, a Keywords Studio headquartered in Tokyo, is leading the data science revolution in the video game industry, developing next-generation machine learning software to operationally predict the behavior of individual players. Models based on convolutional neural networks and conditional inference survival ensembles can reveal information such as the moment and game level at which a certain player will leave the game, how much money they will spend until then, when they will make their next battle or which virtual item they are likely to select. This allows game developers to take preventive actions aimed at maximizing player engagement and to create an optimal, personalized game experience. I will review how these techniques can be used to create customized game events and provide individual recommendations on in-game items and actions. These methods are versatile in that they can adapt to very different types of games and players. By using cloud computing capabilities and big data infrastructure as provided by Amazon Web Services, they can scale to be used with games of any size, as they are also able to deal with really large datasets (petabytes of data). It is the flexibility and scalability of these techniques what makes them particularly well suited for operational environments. ]]>
Abstract Video games define our era. Players generate extremely rich behavioral datasets that constitute an ideal playground to understand human behavior. With this unique source of information, machine learning can not only explore social and consumer dynamics, but also predict users' personalities. Yokozuna Data, a Keywords Studio headquartered in Tokyo, is leading the data science revolution in the video game industry, developing next-generation machine learning software to operationally predict the behavior of individual players. Models based on convolutional neural networks and conditional inference survival ensembles can reveal information such as the moment and game level at which a certain player will leave the game, how much money they will spend until then, when they will make their next battle or which virtual item they are likely to select. This allows game developers to take preventive actions aimed at maximizing player engagement and to create an optimal, personalized game experience. I will review how these techniques can be used to create customized game events and provide individual recommendations on in-game items and actions. These methods are versatile in that they can adapt to very different types of games and players. By using cloud computing capabilities and big data infrastructure as provided by Amazon Web Services, they can scale to be used with games of any size, as they are also able to deal with really large datasets (petabytes of data). It is the flexibility and scalability of these techniques what makes them particularly well suited for operational environments. ]]> Tue, 02 Apr 2019 09:43:46 GMT /slideshow/custommade-games-with-machine-learning-and-big-data/139220357 africaperianez@slideshare.net(africaperianez) Custom-Made Games with Machine Learning and Big Data africaperianez Abstract Video games define our era. Players generate extremely rich behavioral datasets that constitute an ideal playground to understand human behavior. With this unique source of information, machine learning can not only explore social and consumer dynamics, but also predict users' personalities. Yokozuna Data, a Keywords Studio headquartered in Tokyo, is leading the data science revolution in the video game industry, developing next-generation machine learning software to operationally predict the behavior of individual players. Models based on convolutional neural networks and conditional inference survival ensembles can reveal information such as the moment and game level at which a certain player will leave the game, how much money they will spend until then, when they will make their next battle or which virtual item they are likely to select. This allows game developers to take preventive actions aimed at maximizing player engagement and to create an optimal, personalized game experience. I will review how these techniques can be used to create customized game events and provide individual recommendations on in-game items and actions. These methods are versatile in that they can adapt to very different types of games and players. By using cloud computing capabilities and big data infrastructure as provided by Amazon Web Services, they can scale to be used with games of any size, as they are also able to deal with really large datasets (petabytes of data). It is the flexibility and scalability of these techniques what makes them particularly well suited for operational environments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ydperianezgdc2019-190402094346-thumbnail.jpg?width=120&height=120&fit=bounds" /> Abstract Video games define our era. Players generate extremely rich behavioral datasets that constitute an ideal playground to understand human behavior. With this unique source of information, machine learning can not only explore social and consumer dynamics, but also predict users' personalities. Yokozuna Data, a Keywords Studio headquartered in Tokyo, is leading the data science revolution in the video game industry, developing next-generation machine learning software to operationally predict the behavior of individual players. Models based on convolutional neural networks and conditional inference survival ensembles can reveal information such as the moment and game level at which a certain player will leave the game, how much money they will spend until then, when they will make their next battle or which virtual item they are likely to select. This allows game developers to take preventive actions aimed at maximizing player engagement and to create an optimal, personalized game experience. I will review how these techniques can be used to create customized game events and provide individual recommendations on in-game items and actions. These methods are versatile in that they can adapt to very different types of games and players. By using cloud computing capabilities and big data infrastructure as provided by Amazon Web Services, they can scale to be used with games of any size, as they are also able to deal with really large datasets (petabytes of data). It is the flexibility and scalability of these techniques what makes them particularly well suited for operational environments.

Custom-Made Games with Machine Learning and Big Data from Africa Perianez

]]> 155 4 https://cdn.slidesharecdn.com/ss_thumbnails/ydperianezgdc2019-190402094346-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 The state of the art in behavioral machine learning for healthcare /africaperianez/the-state-of-the-art-in-behavioral-machine-learning-for-healthcare ithealthperianez-180424052443
The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease. But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model. In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges. ]]>
The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease. But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model. In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges. ]]> Tue, 24 Apr 2018 05:24:43 GMT /africaperianez/the-state-of-the-art-in-behavioral-machine-learning-for-healthcare africaperianez@slideshare.net(africaperianez) The state of the art in behavioral machine learning for healthcare africaperianez The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease. But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model. In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ithealthperianez-180424052443-thumbnail.jpg?width=120&height=120&fit=bounds" /> The use of smart devices and wearables is becoming increasingly popular. This allows patients to be continuously monitored and provides a huge amount of health-related data that, if properly analyzed, can be used to improve their health by predicting potential future conditions. Advanced machine learning techniques do permit such analysis, and thus serve to forecast the evolution and health challenges of individual patients. This includes, for instance, issues as critical as early detection of heart disease. But, moreover, the whole healthcare sector is currently undergoing a profound transformation. The rich profusion of digital data is fostering a move from more traditional approaches towards a data-driven prevention model. In this talk I survey state of the art methods that allowed an AI-based early diagnosis and risk assessment for individual patients, using information that may include health records, genomic and wearable device data, medical imagery and online physician reviews. I will focus on methods that can be employed to forecast future events affecting a specific patient and serve to evaluate wearable device data and assist healthcare industry in undertaking a patient-focused data-driven preventive approach. Additionally, I introduce how machine-learning-based gamification techniques can be employed to motivate individual users to improve their health condition and achieve personalized challenges.

The state of the art in behavioral machine learning for healthcare from Africa Perianez

]]> 357 7 https://cdn.slidesharecdn.com/ss_thumbnails/ithealthperianez-180424052443-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Game Data Science: The State of the Art /slideshow/game-data-science-the-state-of-the-art/82942982 yokozunadatashanghai-171129034303
In the last few years, we have witnessed a true revolution in the video-game industry, as both traditional video-game platforms and emerging mobile games have become always connected to the Internet. This has contributed to widen the audience for video games (casual gamers) and to the appearance of new economic models (in-app purchases, free-to-play) that are gaining more and more importance in a sector traditionally monetized by expensive one-time purchases or subscriptions. More importantly, this recent paradigm shift allows game developers to collect a huge amount of data in real time while maintaining an active relationship with the players. This has created a broad range of new challenges and opportunities for both data science research and business applications, as demonstrated by the quickly growing number of job openings for data scientists in game companies. To fully take advantage of this new scenario, it is paramount to develop adequate statistical and learning methods that model and predict player behavior, scale to large datasets and allow an intuitive visualization of the results. In this talk, I will survey the state-of-the-art of Data Science in the mobile game industry. First, I will present a general summary of the main techniques to predict player behavior, concentrating on those learning methods that help to reduce user attrition, i.e. churn, which is decisive to increase player retention and raise revenues. Then, I will discuss these techniques from the viewpoint of Game Data Science as a Service. The goal of Silicon Studio is to democratize Game Data Science. Hence, I will show how the proposed methods can make predictions in an operational business environment and easily adapt to different kinds of games and players—namely, to different data distributions. I will focus on flexible techniques that do not need previous manipulation of the data and are able to deal efficiently with the temporal dimension of the churn-prediction problem.]]>
In the last few years, we have witnessed a true revolution in the video-game industry, as both traditional video-game platforms and emerging mobile games have become always connected to the Internet. This has contributed to widen the audience for video games (casual gamers) and to the appearance of new economic models (in-app purchases, free-to-play) that are gaining more and more importance in a sector traditionally monetized by expensive one-time purchases or subscriptions. More importantly, this recent paradigm shift allows game developers to collect a huge amount of data in real time while maintaining an active relationship with the players. This has created a broad range of new challenges and opportunities for both data science research and business applications, as demonstrated by the quickly growing number of job openings for data scientists in game companies. To fully take advantage of this new scenario, it is paramount to develop adequate statistical and learning methods that model and predict player behavior, scale to large datasets and allow an intuitive visualization of the results. In this talk, I will survey the state-of-the-art of Data Science in the mobile game industry. First, I will present a general summary of the main techniques to predict player behavior, concentrating on those learning methods that help to reduce user attrition, i.e. churn, which is decisive to increase player retention and raise revenues. Then, I will discuss these techniques from the viewpoint of Game Data Science as a Service. The goal of Silicon Studio is to democratize Game Data Science. Hence, I will show how the proposed methods can make predictions in an operational business environment and easily adapt to different kinds of games and players—namely, to different data distributions. I will focus on flexible techniques that do not need previous manipulation of the data and are able to deal efficiently with the temporal dimension of the churn-prediction problem.]]> Wed, 29 Nov 2017 03:43:02 GMT /slideshow/game-data-science-the-state-of-the-art/82942982 africaperianez@slideshare.net(africaperianez) Game Data Science: The State of the Art africaperianez In the last few years, we have witnessed a true revolution in the video-game industry, as both traditional video-game platforms and emerging mobile games have become always connected to the Internet. This has contributed to widen the audience for video games (casual gamers) and to the appearance of new economic models (in-app purchases, free-to-play) that are gaining more and more importance in a sector traditionally monetized by expensive one-time purchases or subscriptions. More importantly, this recent paradigm shift allows game developers to collect a huge amount of data in real time while maintaining an active relationship with the players. This has created a broad range of new challenges and opportunities for both data science research and business applications, as demonstrated by the quickly growing number of job openings for data scientists in game companies. To fully take advantage of this new scenario, it is paramount to develop adequate statistical and learning methods that model and predict player behavior, scale to large datasets and allow an intuitive visualization of the results. In this talk, I will survey the state-of-the-art of Data Science in the mobile game industry. First, I will present a general summary of the main techniques to predict player behavior, concentrating on those learning methods that help to reduce user attrition, i.e. churn, which is decisive to increase player retention and raise revenues. Then, I will discuss these techniques from the viewpoint of Game Data Science as a Service. The goal of Silicon Studio is to democratize Game Data Science. Hence, I will show how the proposed methods can make predictions in an operational business environment and easily adapt to different kinds of games and players—namely, to different data distributions. I will focus on flexible techniques that do not need previous manipulation of the data and are able to deal efficiently with the temporal dimension of the churn-prediction problem. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/yokozunadatashanghai-171129034303-thumbnail.jpg?width=120&height=120&fit=bounds" /> In the last few years, we have witnessed a true revolution in the video-game industry, as both traditional video-game platforms and emerging mobile games have become always connected to the Internet. This has contributed to widen the audience for video games (casual gamers) and to the appearance of new economic models (in-app purchases, free-to-play) that are gaining more and more importance in a sector traditionally monetized by expensive one-time purchases or subscriptions. More importantly, this recent paradigm shift allows game developers to collect a huge amount of data in real time while maintaining an active relationship with the players. This has created a broad range of new challenges and opportunities for both data science research and business applications, as demonstrated by the quickly growing number of job openings for data scientists in game companies. To fully take advantage of this new scenario, it is paramount to develop adequate statistical and learning methods that model and predict player behavior, scale to large datasets and allow an intuitive visualization of the results. In this talk, I will survey the state-of-the-art of Data Science in the mobile game industry. First, I will present a general summary of the main techniques to predict player behavior, concentrating on those learning methods that help to reduce user attrition, i.e. churn, which is decisive to increase player retention and raise revenues. Then, I will discuss these techniques from the viewpoint of Game Data Science as a Service. The goal of Silicon Studio is to democratize Game Data Science. Hence, I will show how the proposed methods can make predictions in an operational business environment and easily adapt to different kinds of games and players—namely, to different data distributions. I will focus on flexible techniques that do not need previous manipulation of the data and are able to deal efficiently with the temporal dimension of the churn-prediction problem.

Game Data Science: The State of the Art from Africa Perianez

]]> 661 7 https://cdn.slidesharecdn.com/ss_thumbnails/yokozunadatashanghai-171129034303-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 DSAA 2016 Churn Prediction in Mobile Social Games /slideshow/dsaa-2016-churn-prediction-in-mobile-social-games/68023177 dsaa2016churnprediction-161102022710
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression. ]]>
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression. ]]> Wed, 02 Nov 2016 02:27:10 GMT /slideshow/dsaa-2016-churn-prediction-in-mobile-social-games/68023177 africaperianez@slideshare.net(africaperianez) DSAA 2016 Churn Prediction in Mobile Social Games africaperianez Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dsaa2016churnprediction-161102022710-thumbnail.jpg?width=120&height=120&fit=bounds" /> Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.

DSAA 2016 Churn Prediction in Mobile Social Games from Africa Perianez

]]> 293 3 https://cdn.slidesharecdn.com/ss_thumbnails/dsaa2016churnprediction-161102022710-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://cdn.slidesharecdn.com/profile-photo-africaperianez-48x48.jpg?cb=1568101065 YOKOZUNA data provides a state-of-the-art machine learning engine predicting the behavior of each individual player. A recommendation system and player prediction platform that employs next generation AI algorithms to take game development into the future. Data scientist. Mathematical modeling, data mining, statistical analysis, inverse problems, algorithm development, time series forecasting and machine learning techniques. Experience in industry and research institutions. Project management. High communication skills. Wide international experience. yokozunadata.com https://cdn.slidesharecdn.com/ss_thumbnails/dacern-190410065738-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/data-assimilation-in-numerical-weather-prediction-models/140288872 Data Assimilation in N... https://cdn.slidesharecdn.com/ss_thumbnails/ydperianezgdc2019-190402094346-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/custommade-games-with-machine-learning-and-big-data/139220357 Custom-Made Games with... https://cdn.slidesharecdn.com/ss_thumbnails/ithealthperianez-180424052443-thumbnail.jpg?width=320&height=320&fit=bounds africaperianez/the-state-of-the-art-in-behavioral-machine-learning-for-healthcare The state of the art i...