Self-organizing maps (SOMs) for clustering.Input selection (Hinton graphs, likelihood statistics, brute force, and so on).Architecture selection (grid search, SNC, and so on).Overfitting, early stopping, and weight regularization.Weight learning (backpropagation, conjugate gradient, and so on).MLP types (RBF, recurrent, and so on).Case study: decision tables for textual knowledge verification.Case study: decision tables and diagrams for customer scoring.Decision tables (lexicographical ordering, contraction methods, and so on).Rule types (propositional, oblique, M-of-N, fuzzy, and so on).Case study: using regression trees for loss forecasting.Splitting/stopping/assignment criteria.Recommendations for using decision trees in a business context.Key algorithms: C4.5 (See5), CART, CHAID.Splitting/stopping/assignment decision.Key application areas (CRM, risk management, fraud, online analytics).Analytic model requirements (performance, interpretability, operational efficiency, compliance).Predictive versus descriptive analytics (data mining).Data collection and preprocessing (sampling, missing values, outliers, weights of evidence, and so on).Basic nomenclature (definition of customer, definition of target, and so on).Who should attend Those involved in estimating, monitoring, auditing, or maintaining models for various types of customer intelligence those involved with using data mining techniques for various types of customer intelligence, job titles including business analysts in various settings (for example, risk management, manufacturing, telco, retail, advertising, public, pharmaceutical, and so on), marketing/CRM managers, fraud managers, customer intelligence managers, risk analysts, CRM analysts, marketing analysts, senior data analysts, and data miners Deploy, monitor, and optimally backtest analytical models.Explore a futuristic vision of how emerging data science techniques might change your key business processes. ![]() Ensure the practical application of these techniques to optimize strategic business processes and decision making.Apply a series of powerful, recently developed, cutting-edge analytical and data science techniques.References to background material such as selected papers, tutorials, and guidelines are also provided. ![]() This highly interactive course provides a sound mix of both theoretical and technical insights as well as practical implementation details and is illustrated by several real-life cases. This course helps clarify how to successfully adopt recently proposed state-of-the art analytical and data science techniques for advanced customer intelligence applications. Given recent trends and needs such as mass customization, personalization, Web 2.0, one-to-one marketing, risk management, and fraud detection, it becomes increasingly important to extract, understand, and exploit analytical patterns of customer behavior and strategic intelligence. In today's big data world, many companies have gathered huge amounts of customer data about marketing success, use of financial services, online usage, and even fraud behavior. Professor at KU Leuven (Belgium), and lecturer at the University of Southampton (UK) or Christophe Mues, Ph.D., Professor at the School of Management of the University of Southampton (UK) or Cristian Bravo, Ph.D., Associate Professor, Business Analytics, University of Southampton (UK) or Wouter Verbeke, Ph.D., Assistant Professor, Business Informatics, University of Brussels (Belgium) or Stefan Lessmann, Ph.D., Professor, School of Business and Economics, Humboldt University (Germany)
0 Comments
Leave a Reply. |