报告题目：Interpretable Machine Learning

报告所属学科：管理科学与工程

报告人：Witold Pedrycz（加拿大阿尔伯塔大学）

报告时间：2021年10月8日、2021年10月15日、2021年10月22日、2021年10月29日、2021年11月5日，上午9：00-10：00

报告地点：腾讯会议：32965268237，QQ群：687807963

报告摘要：

(1) Interpretability and Machine Learning

Fundamentals of machine learning. Classes of models, learning paradigms, evaluation measures, defining interpretability. motivating factors. Main requirements.

(2) Interpretability of rule-based models

Classes of rule-based architectures. Modularity properties. Design principles. A taxonomy of interpretation mechanisms: decomposition of conditions, granular conditions and conclusions, symbolic descriptors, linguistic approximation.

(3) Tree structures in machine learning

Decision trees- a generic construct and design strategies. From decision trees to rules. Interpretation mechanisms. Generalized architectures and learning: random forests and gradient boosting in the development of decision trees.

(4) Logic networks and induction of concepts

Logic expressions and logic neurons. Conditional (context) clustering. Concept formation through clustering. Concept refinement via gradient-based learning of logic networks and their interpretation.

(5) Federated learning

Privacy and security requirements. Federated learning of rules. Performance evaluation. Three-tier federated learning and granular rules.

报告人简介：

Witold Pedrycz，加拿大阿尔伯塔大学讲席教授，加拿大皇家科学院院士，波兰科学院外籍院士，电气和电子工程师协会会士。担任Information Sciences和WIREs Data Mining and Knowledge Discovery主编，以及Int. J. of Granular Computing和J. of Data Information and Management共同主编。出版专著18本，H指数109。主要研究方向包括计算智能、模糊建模和颗粒计算、知识发现和数据科学、模式识别、数据科学、基于知识的神经网络和控制工程等。