A two-volume editorial project that is dedicated to the memory of the late Professor Ryszard S Michalski who passed away in 2007. He was one of the fathers of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology.

Introductory Chapters.- Ryszard S. Michalski: The Vision and Evolution of Machine Learning.- The AQ Methods for Concept Drift.- Machine Learning Algorithms Inspired by the Work of Ryszard Spencer Michalski.- Inductive Learning: A Combinatorial Optimization Approach.- General Issues.- From Active to Proactive Learning Methods.- Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms.- Transfer Learning via Advice Taking.- Classification and Beyond.- Determining the Best Classification Algorithm with Recourse to Sampling and Metalearning.- Transductive Learning for Spatial Data Classification.- Beyond Sequential Covering – Boosted Decision Rules.- An Analysis of Relevance Vector Machine Regression.- Cascade Classifiers for Hierarchical Decision Systems.- Creating Rule Ensembles from Automatically-Evolved Rule Induction Algorithms.- Structured Hidden Markov Model versus String Kernel Machines for Symbolic Sequence Classification.- Soft Computing.- Partition Measures for Data Mining.- An Analysis of the FURIA Algorithm for Fuzzy Rule Induction.- Increasing Incompleteness of Data Sets—A Strategy for Inducing Better Rule Sets.- Knowledge Discovery Using Rough Set Theory.- Machine Learning Techniques for Prostate Ultrasound Image Diagnosis.- Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images Using Fuzzy Clustering.- Machine Learning for Robotics.- Automatic Selection of Object Recognition Methods Using Reinforcement Learning.- Comparison of Machine Learning for Autonomous Robot Discovery.- Multistrategy Learning for Robot Behaviours.- Neural Networks and Other Nature Inspired Approaches.- Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks.- Learning and Evolution of Autonomous Adaptive Agents.- Learning andUnlearning in Hopfield-Like Neural Network Performing Boolean Factor Analysis.

This is the first volume of a large two-volume editorial project we wish to dedicate to the memory of the late Professor Ryszard S. Michalski who passed away in 2007. He was one of the fathers of machine learning, an exciting and relevant, both from the practical and theoretical points of view, area in modern computer science and information technology. His research career started in the mid-1960s in Poland, in the Institute of Automation, Polish Academy of Sciences in Warsaw, Poland. He left for the USA in 1970, and since then had worked there at various universities, notably, at the University of Illinois at Urbana – Champaign and finally, until his untimely death, at George Mason University. We, the editors, had been lucky to be able to meet and collaborate with Ryszard for years, indeed some of us knew him when he was still in Poland. After he started working in the USA, he was a frequent visitor to Poland, taking part at many conferences until his death. We had also witnessed with a great personal pleasure honors and awards he had received over the years, notably when some years ago he was elected Foreign Member of the Polish Academy of Sciences among some top scientists and scholars from all over the world, including Nobel prize winners. Professor Michalski’s research results influenced very strongly the development of machine learning, data mining, and related areas. Also, he inspired many established and younger scholars and scientists all over the world. We feel very happy that so many top scientists from all over the world agreed to pay the last tribute to Professor Michalski by writing papers in their areas of research. These papers will constitute the most appropriate tribute to Professor Michalski, a devoted scholar and researcher. Moreover, we believe that they will inspire many newcomers and younger researchers in the area of broadly perceived machine learning, data analysis and datamining. The papers included in the two volumes, Machine Learning I and Machine Learning II, cover diverse topics, and various aspects of the fields involved. For convenience of the potential readers, we will now briefly summarize the contents of the particular chapters.