An introduction to the basic concepts of software engineering. Software life cycle models, requirements analysis, software design, implementation technologies, verification, validation and software modeling.

An introduction to the basic concepts of data science. Data science consists of machine learning algorithms and mathematical models (Supervised vs. unsupervised learning, decision tree pruning, training and testing datasets), big data tools to convert unstructured data into a structured form, and business intelligence to support decision-making.

Students are expected to conduct and present (written and orally) an independent project theoretically and/or experimentally during a semester under the supervision of a faculty member.

This course will provide a foundation in the area of data science based on data curation and statistical analysis. It investigates data concepts, metadata creation and interpretation, general linear method, cluster analysis, and basics of information visualization. The course will cover fundamentals about data and data standards and methods for organizing, curating, and preserving data for reuse, inferential statistics: drawing conclusions and making decisions from data, data analysis tools, and diverse issues around data including technologies, behaviors, organizations, policies, and society.

Relational theory and extensions, such as relational calculus, relational algebra, higher order normal forms; advanced DBMS concepts, such as integrity, recovery, concurrency, security, query optimization; object-oriented databases; distributed databases, related techniques and protocols, such as data replication, data fragmentation, synchronization, load balancing; parallel databases; deductive databases; federated databases and homogeneity/heterogeneity.

The course introduces the fundamentals of software systems development with a focus on analysis and design phases. Data modeling and design principles such as data abstraction, information hiding, modularity, and coupling are viewed in the context of object-oriented paradigm. For object-oriented modeling Unified Modeling Language (UML) is introduced and used throughout the course. A unified process is taken as the basis for development efforts. Issues relating to making the transition from other software development methodologies are examined and risks involved in object-oriented process are discussed.

The course is designed for students to gain an understanding of issues of a real-world software project and enable them to apply their newly learned skills in a practical context. Students will be grouped into teams to undertake a software project with the tools, techniques and skills acquired during their previous course work. Each team will work as a software development group and assume the role of a quality assurance team for another group. The team will be assigned to a client and will interact with that client to engineer requirements, agree upon a design and achieve a successful acceptance test of a software system. Teams will meet on predetermined milestones with their faculty and quality assurance groups to discuss and review results and progress.

Software architectures, architectural styles, patterns, and special architectural solutions. Components and frameworks. Architecture evaluation and selection. Fundamental architecture-driven software qualities. Achieving reusability, interoperability, and change resilience. Architecture description languages. Elements of middleware.

Fundamental theory and practice of the design, implementation, and evaluation of human-computer interfaces. Topics include: life cycles for user interface development; principles of design; design techniques; methods for evaluating interfaces; and techniques for prototyping and implementing graphical user interfaces. Students will perform the analysis, design and evaluation of a user interface of a software system as class project.

The relationship of software testing to quality is examined with an emphasis on testing techniques. Topics include module and unit testing, integration and acceptance testing, statistical testing methods, defining test plans and strategies that map to system requirements. Testing principles, formal models of testing and software testing standards are also examined.

This course is designed to introduce the students to the software management concepts. Topics will include quality managements concepts and project management concepts. Quality management concepts will include process improvement, capability maturity models and statistical process control. Project management concepts will include scope and time management as well as size measurement and effort estimation models. The course also includes the use of related software.

The security of information and communication technologies, fundamental security functions and related cryptographic tools, network security and related problems, protection mechanisms and tools will be evaluated. Then current hot topic security problems will be analyzed  for  “cloud computing”, “web applications and services”, “wireless networks”, etc. and at last security management of information systems will be discussed.

Nature of embedded systems, their role in computer engineering; special and general purpose microprocessor design, embedded microcontrollers, embedded software; real time systems, problems of timing and scheduling; testing and performance issues, reliability; design methodologies, software tool support for development of such systems; problems of maintenance and upgrade; introduction to Application Specific Integrated Circuit (ASIC) Design, VHDL.

The principles of Object-Oriented concepts, such as abstraction, abstract data types, encapsulation, inheritance, polymorphism, aggregation are explained along with the key points of Object-Oriented analysis and design. Unified Modeling Language (UML) is utilized to express the artifacts of Object-Oriented analysis and design. Creational, structural, and behavioral Object-Oriented design patterns are examined and used to solve real-world problems. Resultant Object-Oriented designs are implemented in Java.

‘Software Architectures’ are one of the most significant areas of the software engineering discipline. This course focus on the state of the art event based software architectures. Today’s modern era permeates with phenomenal growth and advancement across a wide range of areas: from science and technology, to medicine and art, as well the social sciences. Amongst all, the notion of “services” plays a critical role to abstract and make available snippets of innovation, that can thereby be used cross-disciplinary to create bigger, better and more interesting innovations. This course arms students with the knowledge and expertise to “design” and “engineer” services using modern technology.

Organization and application of computers and statistical techniques to data processing. Data handling in terms of coding, preparation, acquisition (with and without computers), screening and reduction; summarization, tabulation and analysis; random variables, statistical estimation and hypothesis testing, enumerated data analysis, linear models (regression, correlation, analysis of variance).

Graphical Probability Models. Bayesian Reasoning. Bayesian Networks. Learning in Bayesian Networks. Knowledge Engineering. Temporal Models. Inference in Dynamic Bayesian Networks. Markov Decision Processes.

Linear programming, nonlinear programming, iterative methods and dynamic programming are presented, especially as they relate to optimal control problems. Discrete and continuous optimal regulators are derived from dynamic programming approach which also leads to the Hamilton-Jakobi-Bellman Equation and the Minimum Principle. Linear quadratic regulators, linear tracking problems and output regulators are treated. Linear observer and the separation theorem are developed for controller implementation.

Knowledge discovery and data mining, data warehousing, data preparation and data mining primitives, concept description, mining association rules in large databases, classification and prediction, cluster analysis, web mining, applications in data mining.

This course covers the image processing techniques and approaches for image content analysis. The course content includes memory representation of images, image processing techniques, keypoint extraction and description, image matching and fundamentals of camera geometry.

This course covers advanced topics in machine learning. Possible topics include active learning, reinforcement learning, online learning, non-parametric learning, inductive learning, statistical relational learning, dimensionality reduction, ensemble methods, transfer learning, outlier detection, specific application areas of machine learning, and other relevant and/or emerging topics.

This course covers the fundamental platforms, such as Hadoop, Spark, and other tools, e.g., Linked Big Data, several data storage methods that include HDFS, HBase, KV stores, document database, and graph database, different ways of handling analytics algorithms on different platforms, visualization issues and mobile issues on Big Data Analytics It will discuss large-scale machine learning methods that are foundations for artificial intelligence and cognitive networks and several methods to optimize the analytics based on different hardware platforms, such as Intel & Power chips, GPU, FPGA, etc.

This course covers methods for designing and training deep neural networks. The course content includes the historical evolution of neural networks, their fundamental working principles and image classification and object detection and recognition in images using convolutional neural networks.

This course investigates principles of different kinds of distributed information systems including design and architecture, security, integrity, query processing and optimization, transaction management, concurrency control, and fault tolerance.

This course investigates the methods used for data and knowledge hiding in order to provide privacy of the individuals and organizations. Privacy protection approaches in the data management. Survey on different classes of privacy preserving data mining methods that belong to the knowledge hiding thread in case of data sharing.

This course introduces the fundamental concepts and computational paradigms of large-scale data management. This includes major methods for storing, updating and querying large datasets as well as for data-intensive computing. The course covers concepts, algorithms, and system issues on the topics of parallel and distributed databases, peer-to-peer data management, MapReduce and its ecosystem, Spark and dataflows, datalakes and NoSQL databases.

The course introduces the fundamentals of software engineering from management and software system models perspectives with an emphasis on software development process models, project management techniques and contemporary modeling notations.

The basic programming paradigms will be discussed with emphasis on object oriented programming. The principles will be conveyed using a modern programming language i.e. Java. The background will be used in practice to implement data structures and efficient algorithm development.