Machine Learning & Statistics

Machine Learning & Statistics domain discusses the computerized methods to solve various learning problems, including and not limited to:
Learning Difficulties: Classification, regression, recognition and prediction; Troubleshooting and planning; Reasoning and conclusion; Data mining; Web mining; Natural language processing; Design and diagnosis; Combined optimization; Usage of industrial, financial and scientific from various fields,
Learning Methods: Supervised and unsupervised learning methods (including, neural networks, deep learning, support vector machines, logistical regression, probability networks and other statistical models, grouping, etc.,); Strengthening learning; Evolution-based learning; Information-based learning; Visualization of patterns in data,
Hybrid Methods: Hybrid ANN-GA, hybrid ANN-BFGS, hybrid ANN-BHCS, and other types of hybrid ANN with optimization techniques (including gradient-based methods and meta-heuristic methods) and hybrids between other methods in Machine Learning,
Stochastic and Financial Analysis: stochastic analysis, stochastic model, financial risks, financial issues, price predictions and energy markets.