Pattern Discovery and Disentanglement, a new Paradigm in Machine Learning with Dr. Andrew Wong

Join us for an enlightening discussion led by Dr. Andrew Wong, Distinguished Professor Emeritus, on the groundbreaking research in Pattern Discovery and Disentanglement (PDD) within the field of machine learning. Dr. Wong's paper titled "Theory and Rationale of Interpretable All-in-One Pattern Discovery and Disentanglement System" introduces PDD as a revolutionary paradigm that uncovers intricate associations in relational data, enhancing decision accuracy and interpretability. PDD reduces bias, corrects errors, reveals interpretable patterns in big and small groups, even new and rare groups, and generates a concise knowledge base linking patterns, entities, and primary sources for classification, causal analysis, and functional exploration in healthcare and other human-oriented domains.

Discover how PDD enables the discovery of patterns associated with distinct primary sources, leading to improved predictions without the need of feature engineering and training, enhanced pattern and entity clustering, and the rectification of discrepancies. Attendees will gain valuable insights into pattern-source relations, underlying causal factors, and the implications for clinical studies, ML and practice.

Don't miss this opportunity to explore the forefront of interpretability and pattern disentanglement in ML!