Guide Towards Advanced Data Analysis by Combining Soft Computing and Statistics

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Popular Features. New Releases. Description Soft computing, as an engineering science, and statistics, as a classical branch of mathematics, emphasize different aspects of data analysis. Soft computing focuses on obtaining working solutions quickly, accepting approximations and unconventional approaches. Its strength lies in its flexibility to create models that suit the needs arising in applications. In addition, it emphasizes the need for intuitive and interpretable models, which are tolerant to imprecision and uncertainty.

Statistics is more rigorous and focuses on establishing objective conclusions based on experimental data by analyzing the possible situations and their relative likelihood.

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It emphasizes the need for mathematical methods and tools to assess solutions and guarantee performance. Combining the two fields enhances the robustness and generalizability of data analysis methods, while preserving the flexibility to solve real-world problems efficiently and intuitively. Product details Format Hardback pages Dimensions x x Illustrations note X, p. Other books in this series. Add to basket. Fuzzy Graph Theory Sunil Mathew. Portfolio Analysis Xiaoxia Huang. Fuzzy Semigroups John N. The tutorial concludes by giving an outlook on applications Industry 4. He is co-editor of the Handbook of Evolutionary Computation and co-editor-in.

He is also co-editor-in-chief of Theoretical Computer Science C, and editorial board member and associate editor of a number of journals on evolutionary and natural computing. Thomas has ample experience in real-life applications of optimization and predictive analytics through working with global enterprises such as BMW, Daimler, Honda, Tata Steel, and many others.

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His work with companies focuses on applications of predictive analytics and optimization, in particular for product development, production process optimization, predictive maintenance, anomaly detection, and related areas. His research interests are proposing, improving and analysing stochastic optimization algorithms, especially Evolutionary Strategies and Bayesian Optimization.

In addition, he also works on developing statistical machine learning algorithms for big and complex industrial data. There are a large number of ways that machine learning is advancing biology, biotech and medicine. A key grand challenge in biology is the annotation of genomes. We sequence large numbers of genomes for diverse reasons: from diagnosing and tailoring treatment for diseases to cataloging and mining biodiversity.

We will dive deeply into the problem of predicting the function of proteins and protein families. This is a good illustrative problem at the intersection of biology and ML for several reasons: 1 input data is diverse with networks, sequences, and 3 dimensional structures all in great abundance, 2 protein function is organized into a hierarchy of many thousands of labels, both organized and challengingly rich, and 3 the potential for positive impact is quite high with applications from bioremediation, biosynthesis, ecology, and medicine.

Familiarity with statistics and basic concepts of machine learning.

What is Data Mining (Predictive Analytics, Big Data)

Biology will be introduced and biology knowledge is not needed, but it couldn't hurt to read up on basic concepts like sequence alignment, biological sequence databases, and protein structure. He focuses on creating new methods for using protein structure modeling to interpret genetic variation and new methods for understanding biological networks.

He holds a Ph. Vladimir Gligorijevic joined the Simons Foundation in March as a member of Systems Biology group at the Center for Computational Biology to develop protein function prediction methods using deep learning techniques. Prior to this, Gligorijevic was a research assistant in the computing department at Imperial College London. There, he worked on developing machine learning methods for integration of large- scale, heterogeneous biological data with applications in protein function prediction and precision medicine.

Gligorijevic holds a B. Apache spark, open-source cluster-computing framework providing a fast and general engine for large-scale processing, has been one of the exciting technologies in recent years for the big data development. The main idea behind this technology is to provide a memory abstraction which allows us to efficiently share data across the different stages of a map-reduce job or provide in-memory data sharing.

Our lecture starts with a brief introduction to Spark and its ecosystem, and then shows some common techniques - classification, collaborative filtering, and anomaly detection, among others, to fields particle physics, genomics, social media analysis, web-analytics and finance. If you have an entry-level understanding of machine learning and statistics, and program in Python or Scala, you will find these subjects useful for working on your own big data projects.

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Altan Cakir received his M. During his Ph. However, their research is very much interdisciplinary, with expertise in the group ranging from particle physics, detector development and big data synthesis to economy and industrial applications. He enjoys being able to integrate his research and teaching key concepts of science and big data technologies.

The following lectures on big data are periodically given by Assoc. Big data analytics using cross-domain multi-source datasets allow us to study the phenomena of our interest by fusing views from multiple angles, facilitating us to identify meaningful problems and discover new insights. However, we need methods and techniques to solve the challenges like heterogeneity, uncertainty and high dimensionality in analyzing cross-domain datasets.

In this lecture, I will describe a general framework of cross-domain big data fusion and analytics, and introduce existing works including our own on fusing and analyzing datasets from multiple domains to uncover the underlying patterns, correlations and interactions. Example applications include human and urban dynamics like predicting traffic congestions, optimize demand dispatching in emerging on-demand services, and designing wireless networks.

His research interests include parallel and distributed computing, wireless sensing and networks, pervasive and mobile computing, and big data and cloud computing. He has co-authored 5 books, co-edited 9 books, and published over papers in major international journals and conference proceedings. Cao has also served as chairs and members of organizing and technical committees of many international conferences, and as associate editor and member of the editorial boards of many international journals.

Network-based representation has quickly emerged as the norm in representing rich interactions among the components of a complex system for analysis and modeling. It is thus critical for the network to truly represent the inherent phenomena in the complex system to avoid incorrect analysis results or conclusions. For a variety of complex systems, representing them with the conventional first-order Markov property networks, which is the norm, captures only the first order relationship connections in the underlying data, missing the variable and higher order of dependencies that might be driving the system.

An accurate network representation of the underlying data is a prerequisite for reliable analyses that build upon the network. The limited representation of the first-order network can lead to inaccurate results that rely on the network representation of the underlying data.

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This has spurred recent research that goes beyond the dyadic interactions to higher and variable orders of interactions to more accurately construct the network representation of the underlying data from a complex system. The goal of this tutorial is to provide an introduction to higher order networks and related techniques; and on representation learning for networks inclusive of higher order and first order networks. Xu, Jian, Thanuka L. Wickramarathne, and Nitesh V. Rosvall, Martin, et al. Dong, Yuxiao, Nitesh V.

Chawla, and Ananthram Swami. ACM, Grover, Aditya, and Jure Leskovec. Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering, and director of the research center on network and data sciences at the University of Notre Dame. His research is focused on machine learning and network science with a special initiative on societal impact and advancing the common good. He started his tenure-track career at Notre Dame in , and quickly advanced from assistant professor to an endowed chaired full professor position in He has received numerous awards for research, innovation, and teaching.

His papers have received several outstanding paper nominations and awards. In recognition of the societal and impact of his research, he was recognized with the Rodney Ganey Award and Michiana 40 Under He is the founder of Aunalytics, a data science software and solutions company. The introduction of AI in the midst of society has created new opportunities and new challenges, that include deep issues of fairness, transparency, and human autonomy.

The solution of those new problems cannot be just technical, but there is a role for technical solutions too, within a more general effort to understand what can be done - so that we can safely coexist with intelligent machines in a data-driven society. Machine decisions can affect our rights, and we need to ensure that Artificial Intelligence does not absorb biases by being trained on biased data.

Is our autonomy affected by interacting with intelligent machines designed to persuade us?