Machine learning is concerned with computer programs that automatically improve their performance with past experiences. Machine learning draws inspiration from many fields, artificial intelligence, statistics, information theory, biology and control theory. The course will cover the following topics;concept learning,decision tree learning ,artificial neural networks , instance based learning,evolutionary algorithms ,reinforcement learning ,Bayesian learning , computational learning theory

This course will describe the advantages of quantum computation in order to improve efficiency of classical machine learning methods, and show how to analyze quantum systems using classical machine learning methods.

Intelligent agents, multiagent interactions, agreements, auctions, negotiation, cooperative distributed problem solving, and agent-oriented analysis.

Real Time Systems are comprised software/hardware components embedded into larger systems composed of other subsystems (both mechanical and electronic). These systems are fed by input information from the sensors and are supposed to compute control signals for driving the actuators, resulting in a continuous interaction with the environment. In this course, students learn both the fundamentals of software design and modern design methodologies for real-time systems. This course emphasizes the use of UML diagrams.

Teaching the basic principles of user interfaces. Introduce students to usability models and principles. Having students carry out user and task analyses. Teaching design, prototype development and evaluation through having students complete term projects. Teaching new user interface techniques. Teaching how to carry out user-centric research.

In this course, topics ranging from distributed database design, distributed transaction management and enhanced concurrency control to data replication and distributed query processing and optimization will be discussed.

Foundations of computer graphics, mathematical background, the graphics pipeline, representing 3D models, animation, lighting and materials, texturing and surface detail methods, global illumination, programmable shaders, physics-based methods, mesh deformation techniques, point clouds, non-photorealistic rendering.

The course will cover Sensor network Architecture ,Operating Systems, Physical Layer , Medium Access Control, Network Layer, Power Management, Time Synchronization, Localization, Security, Sensor Network Simulation.

Fundamentals of digital video, image and video compression principles, video coding standards, motion estimation and compensation, transform coding, entropy coding, preprocessing and postprocessing, rate, distortion, complexity and video communication.

The objective of this course is to provide the fundamental concepts of information security, its framework and processes, and to provide insight into abstraction, problem solving and systematic view.

This course is on overview of specification formalisms and techniques used to reason about concurrent and reactive systems.

Floating point arithmetic, computational linear algebra, iterative solution to nonlinear equations, iterpolation, numerical integration, numerical solution of ODEs, computer subroutine packages.

Provides basic knowledge on fundamentals of sparse and redundant representations with theoretical and numerical foundations, as well as practical applications originating from the theory

This course is an in depth study of design patterns and code refactoring techniques which is used to develop complex, maintainable and extendible sofware systems.

This course specifies definitions and concepts, software evolution and maintenance processes, reengineering, refactoring and reuse techniques in software engineering.

Image filtering and deconvolution, eigenimages, noise reduction and restoration, color image processing, multi-resolution processing, image compression, morphological image processing,scale-space techniques, feature extraction and recognition, image thresholding/segmentation, image registration and image matching.

Pattern recognition algorithms and their applications, statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and clustering.

The course will consist of lectures, homework assignments and a project on a topic related to the student's area of interest. We will aim for the right balance of theory and applications. Analysis of Filter Banks and Wavelets, Design Methods, Applications, Hands-on Experience with Software.

Biomolecular and Cellular Principles, Physiological Principles, Biomechanics, Bioinstrumentation, Bioimaging and Signal Processing, Biotechnology, Engineering of Immunity, Biomaterials

Optimal mean-square estimation, Wiener filters. Introduction to adaptive structures and the least squares method. State space models. Kalman filters. Search techniques: Gradient and Newton methods. LMS (least mean squares), RLS (recursive least squares).

Techniques used in the time-frequency analysis of nonstationary signals: Empirical Time Decomposition, Ensemble Empirical Time Decomposition, Multivariate Emprirical Time Decomposition, Variational Mode Decomposition, Intrinsic Time-Scale Decomposition, Short-time Fourier Transform, Fourier Based Synchrosqueezing Transform

Artificial neural networks architectures and learning algorithms. Multi layer perceptron, radial basis function networks and support vector machines. Regression / function approximation, classification and clustering. Artificial neural networks for signal processing, filtering and pattern recognition. Artificial neural networks for system identification and control.

Static optimization with and also without constraints. Optimality conditions. Lagrange multipliers. Karush-Kuhn-Tucker conditions. Steepest-descent and Newton methods. Calculus of variations. Optimal control of discrete time and continuous time systems. Linear quadratic regulator, steady state closed loop control and tracking control. Dynamic programming of both discrete time and continuous time systems.

In this course, localization, object recognition, tracking, sensor fusion, mapping, avoiding obstructions in autonomous vehicles will be explained and Python based perception, motion planning and navigation techniques using Robot operating System (ROS) environment will be taught.

MOS Transistor Theory, CMOS Processing Technology, Circuit Characterization, CMOS Logic Gate Design, CMOS Logic Structures, Dynamic Logic and Clocking Strategies, I/O Structures, Memory, Low Power VLSI Design, Design Strategies, Chip Design Options, Design and Verification Tools, CMOS Testing

Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.

Overview of wireless communications, path-loss shadowing, Wireless channels models, Basic digital modulation techniques over wireless channels.

The course will cover hardware and software design methodologies, use of CAD and simulation tools, assemblers, compilers, debuggers, and programmers. Different microprocessor architectures such as Motorola, Intel, and ARM will be discussed and evaluated, as well as Operating Systems such as uC-Linux and PalmOS. Computer interfaces such as USB, PCI, Ethernet, and Bluetooth will also be discussed in detail.

Hardware and software aspects of embedded DSP systems, interaction between hardware and software, real-time principles and trade-offs in algorithm design and implementation.

The course will cover fast wavelet transform algorithms – relation to filter banks, wavelet packets construction of wavelets, biorthogonality and biorthogonal basis, biorthogonal system of wavelets - construction, and the lifting scheme.

Introduction to video systems, Fourier analysis of video signals, spatio-temporal sampling, motion analysis and motion estimation, video filtering and restoration, video coding and video compression techniques, superresolution, digital TV and video communication standards.

A unification of chaotic dynamics and fractal sets in a dynamical system and set theory background. Sensitive dependency and topological transitivity in invariant sets. From stable fixed points to period doubling, and entrance to chaos. Symbolic dynamics and examples for strange attractors. From cantor set to classical fractals. Self-similarity and fractal dimension. Image encoding by iterated function systems. Randomness in fractal construction. Chaotification. Engineering applications of chaos and fractals.

This is a mandatory course in Electrical and Electronics Engineering Ph.D. Program. The course is in the area of Random Signals, Probability Theory, Stochastic Processes and Random Signal Processing. The course aims to study basic probability concepts, time averages, statistical averages, random variables, moments, CDF, PDF, and PSD functions, random vectors, stationary and ergodic random processes and random signals

This course introduces the concept of heuristics to the students who have already know about mathematical optimization. The topics include basic heuristic constructs (greedy, improvement, construction); meta heuristics such as simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. The basic material on the heuristic will be covered in regular lectures The students will be required to present a variety of application papers on different subjects related to the course. In addition, as a project assignment the students will design a heuristic, write a code of an appropriate algorithm for the problem and evaluate its performance.

Formulation of integer and combinatorial optimization problems. Optimality conditions and relaxation. Polyhedral theory and integer polyhedra. Computational complexity. The theory of valid inequality, strong formulations. Duality and relaxation of integer programming problems. General and special purpose algorithms including branch and bound, decomposition, and cutting-plane algorithms.

Topics of this course include theory, algorithms, and computational aspects of linear programming; formulation of problems as linear programs; duality and sensitivity analysis; primaldual simplex methods; the transportation, transshipment and assignment algorithms; extensions of linear programming; integer programming formulations and solution methods.

The basic concepts of constraint programming. Modeling combinatorial problems in terms of constraints. Constraint consistency and propagation. Global constraints and their propagation algorithms. Search: construction of the search tree, exploration of the search tree, heuristics. Optimization. Advanced techniques: set variables, dealing with redundancy and symmetry. Implementation in a constraint programming language.

This course teaches basic evolutionary algorithms to the students and helps them gain experience in applying some of them. Among the topics of the course are, theoretical foundations of evolutionary algorithms, genetic algorithms, evolutionary operators, particle swarm optimization, and differential evolution algorithm.

Deterministic machine scheduling problems: single stage, open shop, flow shop, and job shop problems with single and parallel machines. Dynamic scheduling problems and priority dispatching. A survey of other scheduling problems. Applications in manufacturing systems.

Definition of efficiency and productivity terms, Introduction to Data Envelopment Analysis (DEA), Presentation of basic DEA models and their applications, Introduction to Stochastic Frontier Analysis (SFA), Presentation of basic SFA model and its applications, Introduction to Malmquist Index (MI), Presentation of MI method and its applications , Introduction to Window Analysis (WA), Presentation of WA method and its applications

This course provides an introduction to statistics with financial applications. Statistical estimation and analysis techniques are provided and illustrated with financial problems.

Linear Programming: Modeling, Solution Methods, Duality in linear programming; Nonlinear programming: First and second order optimality conditions for unconstrained problems, Lagrange multipliers, convexity in mathematical programming, The KuhnTucker theorem; Discrete optimization.

This course will both review and extend a number of basic mathematical tools which are generally useful in applications and are typically assumed as prerequisites for many of the current courses.

This course provides essential materials for analyzing advanced mathematical optimization problem forms, models, and applications by introducing the relevant linear algebra concepts.

Graphs, some special graphs, connectivity, blocks, trees, linear paths, planarity, Kuratowsky theorem, coloring, cromatic numbers, five color theorem, four color theorem, petri nets.

Cryptography is one of the popular topics with direct applications to daily life. Topics include: congruences, factoring, quadratic residues as preliminaries from number theory and continue with cryptography and algebraic geometry.

Biological applications of linear/nonlinear Difference Equations, theory and examples. Biological applications of  Linear/Nonlinear differential equations. Biological applications of partial differential equations. Biological applications of graph theory.

In this course variational formulation of boundary value problems, an introduction to Sobolev spaces and finite element concepts will be taught. Also includes classification of finite elements in onedimensional and twodimensional models.

This course aims to cover an advanced theory and applications of Spectral Analysis.

The course covers basic concepts and applications of Fuzzy Set Theory.