Professional Masters in Informatics

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 equations, curve fitting, integration, optimization, and numerical solutions for ordinary differential equations. The course will place major emphasis on case studies and practical projects to address realistic computational problems using high-performance numerical techniques that utilize recent advances in grid-computing and graphical processing units (GPUs).

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 graphics. The first part focuses on simple engineering concepts for acquisition, restoration, enhancement, and analysis of digital images. The second part covers the basics of three-dimensional computer graphics and the generation of 2D images from 3D models with topics comprising object modeling and representation, rendering, illumination and animation. As a practical course, the lab work includes implementation of the image processing algorithms using Matlab and developing a visualization tool for surface models using C++/OpenGL.

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 the use of different statistical and machine learning techniques. The course will cover these areas. Throughout the whole process, students will be provided with examples that will serve to illustrate concepts being introduced. Students will also learn how to solve real-life problems using state-of-the-art technologies for data analyses.

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 technological and scientific trends in the field and how Big Data and data science are used in modern business enterprises. Use cases of real problems such as networking traffic, text analytics, and financial applications will be addressed in this course.

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 tradeoffs; VC theory; large margins); Neural Networks, Decision Trees, Local Models, Model selection, Combining Multiple Learners, reinforcement learning and adaptive control. During this course, students will learn how to solve real-life problems using state-of-the-art technologies developed for machine learning computing and data analyses.

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. Students who complete this course will understand the architecture of Hadoop clusters at both the hardware and system software levels. Students will learn to apply Hadoop and related Big Data technologies in developing analytics and solving the types of problems faced by enterprises today. The course strongly emphasizes implementation of big data routines using Java and Python.

This course focuses on critical aspects of the software development life cycle that have significant influence on the overall quality of the software system including techniques and approaches to software design, quantitative measurement and assessment of the system during implementation, testing, and maintenance, and the role of verification and validation in assuring software quality.  

The course includes an overview of the history and current and future trends and issues in processor design, as well as performance measurement and enhancement techniques. Topics covered include pipelining, parallelism, multiprocessors, cache & memory issues, and interconnections networks.

This course introduces random processes and their applications from a discrete-time point of view, and discusses the continuous-time case when necessary. The course covers the basic concepts of random variables, random vectors, stochastic processes, and random fields. It moves on to common random processes including the white noise, Gaussian processes, Markov processes, Poisson processes, and Markov random fields. Advanced topics are also covered including estimation theory and optimal filtering including linear prediction, Wiener and Kalman filtering, linear models and spectrum estimation.

This course covers mathematical models for channels and sources. The basic concepts of entropy, relative entropy, and mutual information are defined, and their connections to channel capacity, coding, and data compression are presented. Limits for error-free communication, information theory also presents limits for data compression, information, and data compression, Topics also include channel capacity, Shannon's theorems and rate-distortion theory. 

This course covers fundamental concepts in the design and implementation of computer networks. Examples are drawn primarily from the Internet protocol suite. In the first part of the course, we will cover layered networking models, application layer protocols, transport layer protocols, Internet Protocol (IP), and internetworking. In addition, advanced topics such as wireless networks and network security will be introduced. In the second part of the course, the course focuses on queuing theory and modelling networks using queues. Topics covered include Birth-death processes, Poisson queues, and networks of queues.

This course introduces the fundamentals of operations research, including different techniques for modelling and problem-solving. The course will emphasize model-formulation skills, the mathematical procedures of linear programming, network flows, dynamic programming, game theory Markov chains, queuing models, and other problem-solving techniques. 

This course focuses on the theory and applications and algorithms of convex optimization. It focuses on recognizing and solving convex optimization problems that arise in many engineering fields. It is divided into three parts; Mathematical background, convex optimization theory, and its applications. The Mathematical background part reviews relevant topics in linear Algebra that are necessary for the students to complete the course. The theory part covers the basics of convex analysis and convex optimization problems such as linear programming (LP), semi-definite programming (SDP), second order cone programming (SOCP), and geometric programming (GP), as well as duality in general convex and conic optimization problems. The third part of the course focuses on engineering applications of convex optimization, from systems and control theory to estimation, data fitting, and information theory.

The course covers the network evolution and development, network architectures, network topologies and technologies, layered protocol design, OSI model, MAC protocols, multiplexing, switching, flow control, IP networking, addressing, IPv4 vs. IPv6, transmission protocols, TCP/IP networking, routing and queuing, Domain Name System, network management, network performance evaluation, Quality of Service architecture, IntServ, DiffServ, MPLS, Multicasting, VPNs, multimedia transmission protocols, Traffic Engineering, this is in addition to the state-of-the-art of networking applications and services like Cloud Computing, P2P networking & Ubiquitous Computing.

The course targets students and IT professionals looking for understanding cloud computing basics, environment and architecture. The course covers cloud infrastructure, cloud deployment and service models. This is in addition to basic concepts of traditional data centers, virtualization, migrating to cloud computing and deciding for optimal cloud deployment model.