Track Data Mining and Machine/Deep Learning Track Chair Prof. Dr. Hasan Dağ, Kadir Has University Data science expresses that previous data processing applications are not sufficient to process larger and more complex data sets. The relatively recent concepts of data mining, machine learning, and deep learning offer a new set of techniques and methods. Today, researchers and companies are dealing with and experimenting with various methods of deriving value, such as machine learning, data mining, artificial intelligence, and deep learning. Data Mining and Machine / Deep Learning track aims to contribute to fields that are related to analytics of data that based on different data types. New approaches, applications, models or methods related to the topic of this track. Prof. Dr. Hasan Dağ obtained his bachelor degree in electrical engineering from Istanbul Technical University, Istanbul, Turkey and obtained both his master and PhD degrees both in University of Wisconsin-Madison in electrical and Computer Engineering. His area of interest in general is computational science, data science and smart grid. His recent research areas are Data Science, Big Data, Cyber Security, and their application to Smart Grid. He holds the directorate position of research resources, while at the same time holding the position of the head of Management Information System at Kadir Has University, Istanbul, Turkey. He has also been appointed the directorate position of Research Center for Cyber Security and Critical Infrastructures. Unsorted Evaluation of Call Center Efficiency Using Text Mining Approach Veri Madenciliği Üzerine Endüstriyel Bir Durum Çalışması Balıkçılık Endüstrisinde Kullanılan Büyüme Modellerinde Geleneksel Yaklaşımlar ile Yapay Sinir Ağlarının Yaklaşımlarının Karşılaştırılması Integration of the Google Analytics tool into the data pre-processing layer for WEB Usage Mining: A case study Siğil Tedavisinde Kullanılan Immunotherapy Yönteminin Uygunluğunun Bayes Yöntemi ile Tespiti Predicting Diffusion Reach Probabilities via Representation Learning on Social Networks Finding a Model that Provides High Profits with Web Usage Mining: A Case Study Derin Öğrenme ile Görüntü Kümeleme K-Means vs. Fuzzy C-Means: A Comparative Analysis of Two Popular Clustering Techniques on the Featured Mobile Applications Benchmark A New Approach to Development of Recommendation Systems with Opinion Mining on Turkish User Reviews