Informatics

Takes a holistic approach that is not only about using tools to find information online but also how to link all the information and transform it into presentable and actionable intelligence. You will also learn how to secure your information online to prevent it being discovered by these reconnaissance methods.

Fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits.

Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection.

A collection of important research findings on the latest developments in network modeling for optimization of smart cities. Such models can be used from outlining the fundamental concepts of urban development to the description and optimization of physical networks, such as power, water or telecommunications.

Takes a fresh look at classical information theory and introduces a different point of view for research and development engineers and graduate students in communication engineering and wireless communication.

Takes a holistic approach that is not only about using tools to find information online but also how to link all the information and transform it into presentable and actionable intelligence. You will also learn how to secure your information online to prevent it being discovered by these reconnaissance methods.

Fuses results from complexity modeling and information theory that allow both meaning and design difficulty in nature to be measured in bits.

Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection.

A collection of important research findings on the latest developments in network modeling for optimization of smart cities. Such models can be used from outlining the fundamental concepts of urban development to the description and optimization of physical networks, such as power, water or telecommunications.