Lectures

Choice Models in Data Analysis and their Applications – Part I

Several models, based on choice procedures and the superposition principle are developed and applied for different methods of smart data analysis. Several applications are discussed, in particular, to the data on retailer data analysis, on banking, to the data provided by Microsoft and to the tornado prediction. For efficiency evaluation of tornado prediction, the constructed model has been tested on real-life data obtained from the University of Oklahoma (USA). It is shown that the constructed tornado prediction model is more efficient than all previous models.

Choice Models in Data Analysis and their Applications – Part II

Several models, based on choice procedures and the superposition principle are developed and applied for different methods of smart data analysis. Several applications are discussed, in particular, to the data on retailer data analysis, on banking, to the data provided by Microsoft and to the tornado prediction. For efficiency evaluation of tornado prediction, the constructed model has been tested on real-life data obtained from the University of Oklahoma (USA). It is shown that the constructed tornado prediction model is more efficient than all previous models.



Network-based Data Analysis

Many real-life complex systems can be conveniently represented using networks, with the nodes corresponding to the system’s components and the arcs describing their pairwise interactions. Analyzing structural properties of a network model provides useful insights into the underlying system’s behavior. This talk introduces the basics of the network-based approach to analysis of large data sets and discusses several applications of this methodology.

Cluster-detection Methods in Network-based Data Analysis

Cluster analysis is an important task arising in network-based data analysis. Perhaps the most natural model of a cluster in a network is given by a clique, which is a subset of pairwise-adjacent nodes. However, the clique model appears to be overly restrictive in practice, which has led to introduction of numerous models relaxing various properties of cliques, known as clique relaxations. This talk focuses on a systematic cluster analysis framework based on clique relaxation models.



Machine Learning for Medical Science
Data Assimilation with Machine Learning


Machine Learning for Education and Education for Machine Learning

We now know how to train a program to play world-champion-level chess or Go, just by simulating moves into the future and reinforcing the best ones. But we can’t train a human student to learn in the same way. This talk examines what we do know about applying data to education, and what might be coming in the future.

Software Engineering for Machine Learning and Machine Learning for Software Engineering

The software industry has built up a formidable set of tools for software development over the last half century. But we are just starting to understand what tools are needed to build software with machine learning, not hand-coding. Some of those tools will themselves make use of machine learning.