Machine Learning And Business Problem-Solving

For our lab, we began digging into the application of machine learning beginning in 2014, exploring its application in everything from supply chain optimization to factory automation and retail, including predicting terrorist attacks. Where we can apply knowledge for a given domain and weave it into a learning algorithm for the sake of doing non-deterministic pattern recognition, machine learning grounded in only statistics (not symbology, logic, or evolutionary) can readily improve upon guessing. Learning from a productive data set, and where overfitting is sufficiently avoided or mitigated, a learning algorithm can recognize patterns and generalize to cases not yet encountered. Such explorations for us started more than two years ago with SAP NS2 and ConvergentAI (formerly AxxonAI) where we find the project team's proof-of-concept (POC) results remain relevant today, but applicable to problem-solving the same way in other domains. While conceptually different, a strong relationship exists between machine learning and analytics where machine learning uses data and learning algorithms (supervised and unsupervised) to optimize a model based on performance and prior experience.