Linear Algebra Review, Normal Matrices, Quadratic Forms and Semidefinite Matrices, Inner Product and Norm Spaces, State Space Descriptions for Continuous and Discrete Time Systems, Controllability, Observability, Stability, Realization Theory.
Review of multi-dimensional sampling theory, aliasing, and quantization, fundamentals of color, human visual system, 2-D Block transforms, DFT, DCT and wavelets. Image filtering, edge detection, enhancement, and restoration. Basic video file formats, resolutions, and bit rates for various digital video applications. Motion analysis and estimation using 2D and 3D models. Motion-compensated filtering methods for noise removal, de-interlacing, and resolution enhancement. Digital image and video compression methods and standards, including JPEG/JPEG2000 and MPEG-1/2 and 4. Content-based image and video indexing and MPEG-7.
Characterization of communication signals & systems, digital modulation schemes, optimum reception for the additive white Gaussian noise (AWGN) channel, signal design for band-limited channels, Nyquist criterion, intersymbol interference (ISI), optimum reception for channels with ISI and AWGN, linear equalization, decision feedback equalization, adaptive equalization, channel capacity & coding, linear block codes, convolutional codes, multichannel and multicarrier systems, spread spectrum signals for digital communications, multiuser communications. Design oriented exercises using computer aids.
Introduction to mathematical formulations and computational techniques for the modeling and simulation of engineering and other kinds of systems, including electronic, mechanical, biological, biochemical, virtual, abstract and multi-domain dynamical systems. Applications from various engineering disciplines and the sciences. Matrix formulation of equations for linear problems. Formulation of equations for nonlinear problems & linearization. Numerical solution of linear algebraic equations. Gaussian elimination, computations with sparse & structured matrices. Floating point number representation & arithmetic. Numerical conditioning, ill-conditioned problems. Numerical solution of nonlinear algebraic equations. Fixed point iteration & Newton’s method in one dimension. Newton’s method for system of coupled nonlinear algebraic equations. Improving convergence of Newton’s method. Numerical solution of ordinary differential equations. Forward & backward Euler, trapezoidal rule. Multistep methods, accuracy & stability. Implicit vs explicit techniques, region of stability, stiff problems.
Supervised Adaptive Filtering (Least Mean Square, Recursive Least Squares, Fast Algorithms), Unsupervised Adaptive Filtering (Higher Order Statistics Methods, Blind Deconvolution, Blind Source Separation, Independent Component Analysis), Model Based Signal Processing (Sparsity, Boundedness, Subspace Algorithms), Multirate Signal Processing, Filter-banks and Wavelets.
The optoelectronic devices such as light-emitting diodes, lasers, photodetectors, etc. are widely used in modern lighting, information and telecommunication technologies. We will discuss the following topics with mathematical derivations and exciting practical examples: wave nature of light, dielectric waveguides and optical fibers, semiconductor science and light-emitting diodes, stimulated emission devices: optical amplifiers and lasers, photodetectors and image sensors, and polarization and modulation of light.
Design, simulation, and optimization of integrated photonics structures, the notion of fabless silicon photonics, metal and dielectric waveguides, planar waveguide modes, coupled mode theory, integrated passive couplers and splitters, Mach-Zehnder Interferometers, ring and disk resonators, adiabatic couplers, Bragg gratings, grating and edge couplers, photonic crystal waveguides and structures, principles of integrated modulator and detector operation, fabrication-dependent design considerations, use of transfer matrix, eigenmode expansion, and finite difference time-domain simulation techniques throughout the semester, design oriented and simulation based assignments and term project.
Recent applications of wearable devices, design fundamentals of physiological monitoring systems, overview of physiological signals (SCG, ECG, PPG, EEG, EMG, etc.). Tools for biomedical signal analysis: digital filters, feature extraction, data visualization and machine learning (regression and classification).
Mathematical questions around machine learning and deep learning, geometrical aspects of high dimensional learning models, geometric stability priors, geometry of optimization, smooth and non-smooth optimization method including gradient methods, proximal methods, mirror descent, Nesterov's acceleration, ADMM, stochastic optimization, variance reduction, and distributed optimization.
The course aims to help students advance their reading, listening, writing and speaking skills (moving from B2 level English to C1-C2) through exposure to a variety of texts in English dealing with themes varying from citizenship, sense of responsibility, integrity, civic involvement, community work, ethical standards in education, business and management, use of technology, and other related academic fields. Main objectives: communicating effectively in written and oral form, focusing on academic conventions and style to produce and present academic work; developing critical thinking skills, formulating an argument and supporting ideas through finding appropriate and effective sources, synthesizing information, evaluating the reliability of information.
Introduction to probability, sets, conditional probability, total probability theorem and Bayes rule; Independence, counting; Discrete random variables, functions of random variables, expectation, mean and variance; Continuous random variables, probability density functions, and cumulative distribution functions; Multiple random variables; Sums of random variables; Limit theorems; Covariance and correlation; Introduction to Stochastic Processes
Introduction to probability, sets, conditional probability, total probability theorem and Bayes rule; Independence, counting; Discrete random variables, functions of random variables, expectation, mean and variance; Continuous random variables, probability density functions, and cumulative distribution functions; Multiple random variables; Sums of random variables; Limit theorems; Covariance and correlation; Introduction to Stochastic Processes
Descriptive statistics; measures of association, correlation, simple regression; probability theory, conditional probability, independence; discrete and continuous random variables; probability distributions; functions of random variables; sampling distributions; estimation; inference (confidence intervals and hypothesis testing). Topics are supported by computer applications and specific examples from engineering applications.
Overview of corporate dynamics, including career paths, organizational structure and behavior in large organizations, corporate culture, decision-making process (organs, levels of authority, meetings, crisis and stress management), customer-focused organization and engineering ethics. There will be several case studies. There will also be high profile speakers from the corporate world to convey their real world experiences.
A broad introduction to machine learning covering regression, classification, clustering, and dimensionality reduction methods; supervised and unsupervised models; linear and nonlinear models; parametric and nonparametric models; combinations of multiple models; comparisons of multiple models and model selection.
Effective assessment of data by applying statistics and computing techniques. Introduction of major data descriptors. Applying spreadsheet tools to facilitate data analysis and consequent decision making. Introduction to flowcharts and algorithms. Algorithmic reasoning for computer programming. Emerging information and computing technologies and the future of computing.
Introduction to basic mathematical concepts (including sets, counting, permutations, combinations, graph theory, basic probability and statistics) and simple problem solving methods using the following interesting examples: Hilbert Hotel, pigeonhole principle, Fibonacci numbers, stable marriage problem, four color theorem, traveling salesman problem, art gallery problem, schoolgirl problem, seven bridges problem, three prisoners problem, mathematical ideas in magic tricks, mathematics of gambling.
Problematic issues concerning human rights in Türkiye; the European Convention on Human Rights; the substantive rights laid down in the Convention (right to life, prohibition of torture, right to liberty and security, right to a fair trial, protection of private life, freedom of expression); recent legal and political developments in Türkiye from the viewpoint of human rights; current topics of debate.
Understanding how we experience freedom, justice, equality, rights, good&evil, judgments, and discrimination in our everyday life from the corner of a grocery store to a doctor's office, to a court-house or to a class at the university. Analyzing the various ways of ethical reasoning already happening in our everyday interactions in order to enrich and sometimes to challenge the philosophical theories of ethics. Analyzing the already existing theories of ethical reasoning in the history of philosophy to challenge our at times non-reasoning habits. Connections between theory and practice in everyday life through very open discussion of everyday examples in connection to our readings of ethical reasoning from Plato, Aristotle, Mill, Kant, Marx, Nietzsche, Sartre, Arendt, De Beauvior, etc.
Understanding how we experience freedom, justice, equality, rights, good&evil, judgments, and discrimination in our everyday life from the corner of a grocery store to a doctor's office, to a court-house or to a class at the university. Analyzing the various ways of ethical reasoning already happening in our everyday interactions in order to enrich and sometimes to challenge the philosophical theories of ethics. Analyzing the already existing theories of ethical reasoning in the history of philosophy to challenge our at times non-reasoning habits. Connections between theory and practice in everyday life through very open discussion of everyday examples in connection to our readings of ethical reasoning from Plato, Aristotle, Mill, Kant, Marx, Nietzsche, Sartre, Arendt, De Beauvior, etc.
A historical introduction to ethical reasoning in order to develop skills to examine our lives. Recognition of the principal problems of ethics in a variety of works. Reading, thinking and writing critically about ethical issues and problems. Examination of theory of knowledge, origins of ethics, ethical responsibility and critiques of ethical theories under the guidance of Plato, Aristotle, Descartes, Kant and Nietzsche.
A historical introduction to ethical reasoning in order to develop skills to examine our lives. Recognition of the principal problems of ethics in a variety of works. Reading, thinking and writing critically about ethical issues and problems. Examination of theory of knowledge, origins of ethics, ethical responsibility and critiques of ethical theories under the guidance of Plato, Aristotle, Descartes, Kant and Nietzsche.
A historical introduction to ethical reasoning in order to develop skills to examine our lives. Recognition of the principal problems of ethics in a variety of works. Reading, thinking and writing critically about ethical issues and problems. Examination of theory of knowledge, origins of ethics, ethical responsibility and critiques of ethical theories under the guidance of Plato, Aristotle, Descartes, Kant and Nietzsche.
A historical introduction to ethical reasoning in order to develop skills to examine our lives. Recognition of the principal problems of ethics in a variety of works. Reading, thinking and writing critically about ethical issues and problems. Examination of theory of knowledge, origins of ethics, ethical responsibility and critiques of ethical theories under the guidance of Plato, Aristotle, Descartes, Kant and Nietzsche.