Personal Website of George V. Moustakides
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Scientific Activities
Publications
Teaching
Courses
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CS Rutgers
CS-596: Topics in the Foundations of Computer Science, Foundations of Computer and Data Science
Graduate, Fall semester, Elective.
Topics: Linear Algebra: Matrices and vectors, Matrix operations, determinants, inverse, eigenvalue/eigenvector decomposition, functions of matrices, extension to block matrices. Singular value decomposition and other decompositions. Finite and infinite dimensional linear spaces, orthogonality principle. Probability Theory: Probability space, random variables, expectations. Conditional probability, conditional expectation. Basic decision and estimation techniques. Inequalities.
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CS-535: Pattern Recognition, Theory and Applications
Graduate, Fall semester, Elective.
Topics: Frequent itemsets and association rules. Density estimation. K-means method. Gaussian mixtures and the expectation/maximization method. Factor analysis. K-memoids and Hierarchical clustering. Evaluation metrics & practical issues. Distance/Similarity measures & metric learning. Principal component analysis (PCA) & Singular value decomposition (SVD). Spectral & graph clustering. Kernel principal components & Independent component analysis (ICA) & Canonical correlation analysis (CCA). Recommendation systems. Latent variable models & probabilistic topic models. Matrix factorization, Tensor factorization. Time-series modeling, model selection. General clustering theory.
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Past Courses/Lectures
Computer Vision: Department of Computer Science, Rutgers University, New Brunswick, NJ, USA, Fall 2017 and Spring 2018,  2017  (graduate course).
Digital Signals and Filters: Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USA, Fall 2016,  2016  (graduate course).
Optimization modeling: Department of Management Science & Information Systems, Business School, Rutgers University, New Brunswick, Fall 2015, (undergraduate course).
Optimal stopping, Application to sequential detection: Coordinated Science Lab, University of Illinois, Urbana-Champaign, Fall 2014,  Videos  (series of 11 one-hour lectures).
Detection and estimation theory: Department of Electrical Engineering, Columbia University, Fall  2009   and  2012  (graduate course).
Sequential detection of changes: Department of Statistics, Columbia University, Summer  2007  (series of lectures).
Estimation Theory and Stochastic Control: Department of Electrical and Computer Engineering, University of Patras, Greece, (undergraduate course).
Detection & Estimation: Department of Electrical and Computer Engineering, also Department of Computer Engineering and Informatics, University of Patras, Greece, (graduate course).
Fuzzy & Neural Control: Department of Electrical and Computer Engineering, University of Patras, Greece, (undergraduate course).
Digital Communications: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course).
Signals and Systems: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course).
Control Theory: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course).
Linear Algebra: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course).
Circuit Theory: Department of Computer Engineering & Informatics, University of Patras, Greece (undergraduate course).
Measure theory and probability: Department of Computer Engineering & Informatics, University of Patras, Greece (series of lectures).
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