Constantinos Boulis

DETAILED GRADUATE COURSES TRANSCRIPT

Since I believe that the titles that appear in the University of Washington transcript are not very descriptive, I would like to take some extra space and present a very short description of the graduate courses I registered for. The description is the syllabus provided by the instructor.

Original Title / Short description / Grade
EE505 PROB & RAND PROCESSES / Foundations for the engineering analysis of random processes: set theoretic fundamentals, basic axioms of probability models, conditional probabilities and independence, discrete and continuous random variables, multiple random variables, sequences of random variables, limit theorems, models of stochastic processes, noise, stationarity and ergodicity, Gaussian processes, power spectral densities. / 4.0
EE518 DGTL SIGNAL PROCESS / Digital representation of analog signals. Frequency domain and Z-transforms of digital signals and systems design of digital systems; IIR and FIR filter design techniques, fast Fourier transform algorithms. Sources of error in digital systems. Analysis of noise in digital systems. / 3.4
EE516 COMP SPEECH PROC / Introduction to automatic speech processing. Overview of human speech production and perception. Fundamental theory in speech coding, synthesis and reproduction, as well as system design methodologies. Advanced topics include speaker and language identification and adaptation. / 3.8
EE586 DIG VID COD SYS / Introduction to digital video coding algorithms, standards, and systems. Theoretical and practical aspects of important topics on digital video coding algorithms, motion estimation, video coding standards, systems and implementation issues, and visual communications. / 3.5
EE506 COMM THRY I / Review of stochastic processes. Communication system models. Channel noise and capacity. Optimum detection, modulation and coding, convolutional coders and decoders. Typical channels, random and fading channels. Waveform communication, optimum filters. / 3.8
EE566 COMP-COMM NETS II / Local area, metropolitan area, satellite, and packet radio networks; routing algorithms for wide area networks; optimal design of packet-switched networks; congestion and flow control; fast packet switching; gigabit networks / 4.0
INDE599 SPECIAL TOPICS IE / Modeling and analysis of random processes encountered in engineering applications. Stationarity and ergodicity. Harmonic analysis, power spectral densities. Karhunen-Loeve expansions. Poisson, Gaussian, and Markov processes. / 4.0
AA581 DIGITAL CONTROL I / Discrete-time and sampled-data systems, the Z-transform, frequency domain properties; sampling, D/A and A/D conversion issues; controller design via discrete-time equivalents to continuous-time controllers, by direct-digital root locus, by loop shaping, and via state feedback and observers. / 3.4
EE596A ADV TOPICS S&I PROC / Covers classification and estimation of vector observations, including both parametric and nonparametric approaches. Includes classification with likelihood functions and general discriminant functions, density estimation, supervised and unsupervised learning, feature reduction, model selection, and performance estimation. / 4.0
EE596B ADV TOPICS S&I PROC / Bayesian networks, Markov random fields, factor graphs, Markov properties, standard models as graphical models, graph theory (e.g., moralization and triangulation), probabilistic inference (including pearl's belief propagation, Hugin, and Shafer-Shenoy), junction threes, dynamic Bayesian networks (including hidden Markov models), learning new models, models in practice. / 4.0
MEDED598 SPC TPC IN INFOR / Introduction to computational linguistics and natural language processing. Context-free grammars, natural language parsing, morphology, pragmatics, lexical and discourse semantics. Applications of NLP to information retrieval and text mining. / 3.8
CSE590 SPEC TPCS COMP SCI / Computational Biology seminar with final project. / CR
EE520 SPTCR ANLYS TME SER / Estimation of spectral densities for single and multiple time series. Nonparametric estimation of spectral density, cross-spectral density, and coherency for stationary time series, real and complex spectrum techniques. Bispectrum. Digital filtering techniques. Aliasing, prewhitening. Choice of lag windows and data windows. Use of the fast Fourier transform. The parametric autoregressive spectral density estimate for single and multiple stationary time series. Spectral analysis of nonstationary random processes and for randomly sampled processes. Techniques of robust spectral analysis. / 3.9
CSE590 SPEC TPCS COMP SCI / Bayesian networks, Markov networks, Markov chain Monte Carlo, Belief propagation, Mixture models, Maximum likelihood and Bayesian estimation, the EM algorithm, Hidden Markov models, Dynamic Bayesian networks, Particle filters / 3.7

For the CSE590 SPEC TPCS COMP SCI there was no Grade since the Credit/No Credit policy was opted for. CR stands for Credit.

The EE596A ADV TOPICS S&I PROC course has been renamed to “Introduction to Statistical Learning “. In Winter 2004, I served as the course’s grader.

The EE596B ADV TOPICS S&I PROC has been renamed to “Graphical Models in Pattern Recognition”.

In Spring 2005, I served as the grader for the graduate course EE 517 “Statistical Language Processing”.