This course aims be a comprehensive introduction to the basic concepts and algorithms of digital processing of visual information that would be utilized in the most prominent applications such as medical imaging, remote sensing, space exploration, surveillance, gaming and entertainment, manufacturing and robotics. The course is divided into two closely-related parts: image processing and computer

This course covers numerical analysis and solution techniques for common scientific and engineering problems and provides essential foundation for important computational subject areas such as medical imaging, bioinformatics, financial modeling, to name a few. The course covers a variety of topics including numerical approximations and errors, roots of equations, systems of linear algebraic

This course provides an introduction to machine learning and statistical data analysis. The first part of the course covers topics such as parameter estimation, hypothesis testing and regression analysis. The second part includes machine learning topics such as supervised learning; unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance

The capability of collecting and storing huge amounts of versatile data necessitate the development and use of new techniques and methodologies for processing and analyzing big data. This course provides a comprehensive covering of a number of technologies that are at the foundation of the Big Data movement. The Hadoop architecture and ecosystem of tools will be of special focus to this course

This course acts as an applied course where students can develop on their combined knowledge of BigData technologies (e.g. Hadoop, Spark, etc.) and Data Science (e.g. Statistics, Machine Learning, etc.) and understand how such combination is used to solve real-world applications. In addition to this main goal, the course has the additional goal of familiarizing students with the latest

This course provides an introduction to data mining concepts over structured and un-structured data with special emphasis on practical applications of this important research area. Data Mining usually involves the extraction and discovery of useful knowledge from raw data. The discovery process, also known as knowledge discovery, includes feature selection, data cleaning, and coding and entails