| Background Introduction at analytics Probability and statistics basics Mathematical logic basics Introduction to uncertainty Theory of algorithmic complexity Performance measures Statistics for Descriptive and Predictive Analytics Descriptive statistics Inferential statistics Dependence methods Interdependence methods Potential enhancement and augmentation of statistical techniques Analytics Problem Modeling in Symbolic Artificial Intelligence Approaches to handling uncertainty Deductive, inductive, and abductive reasoning Ontology and knowledge reasoning Ontology and knowledge representation Rule-based system Bayesian belief networks (BBN) Case-based reasoning Machine Learning/Data Mining for Descriptive and Predictive Analytics Generative vs. discriminative models Supervised, unsupervised and semi-supervised learning Symbolic techniques Sub-symbolic techniques Algebraic Bragging and boosting Time-Series Modeling for Predictive Analytics ARMA/ARIMA ARCH/GARCH Hidden Markov models (HMM) Dynamic Bayesian networks (DBN) Kalman filtering and extensions Particle filtering Prescriptive Analytics and Decision Support Test hypothesis Expected utility theory (EUT) Influence diagrams Symbolic argumentation Reinforcement learning Markov decision process (MDP) and partially ordered MDP Text Analytics Natural language processing (NLP) Text classification Information extraction and representation in RDF Case Studies Text document classification Image classification Topic detection Customer segmentation Syndromic surveillance Clinical state estimation Risk assessment via text analytics Opinion mining and sentiment analysis Index References |
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