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Courses

UNDERGRADUATE COURSES
  · AIS 404: Multimedia Data Management 
Processing, archiving, and searching multimedia information including documents, one-dimensional signals, still and moving images (video) in information systems and the Internet. Classic models of information retrieval (binary, relational, probabilistic), information clustering and clustering algorithms (partitional, hierarchical, hybrid algorithms), clustering applications grouping in document collections. Visualization of one-dimensional signals and images in multimedia systems. Feature extraction (color, texture, shape, and spatial relationships) from images. Retrieval methods for one-dimensional signals and images. Indexing techniques in information systems for documents and multimedia information (inverted files, k-d trees, grid files, R-trees). Design of information systems on the Internet, management and analysis of information on the Internet (PageRank and HITS methods). Basic processing techniques and analysis of still and moving images (video) in information systems. Compression techniques, JPEG, MPEG-1, 2, 4, 7 standards.
Instructor: Prof. Euripides G.Μ. Petrakis  
     
  · AIS 405: Machine Vision 
Basic principles and methodology of machine vision with emphasis on algorithms and applications of machine vision. Image formation, mathematical, geometric, colour, frequentist, discrete models. Basic image processing techniques (filtering, enhancement, normalization). Edge detection, first and second derivative operators. Image segmentation, methods for segmenting or enhancing regions and edges, thresholding techniques. Advanced segmentation techniques (merging and splitting regions and edges, relaxed ordering, Hough technique). Techniques for processing binary images, distance transforms, morphological operators, and region labeling. Analysis, representation, and recognition of images. Representation of edges and regions, representation and recognition of shapes, representation and recognition of structural content. Texture analysis and recognition, structural and statistical methods. Dynamic vision, estimation of motion, optical flow, and trajectory.
Instructor: Prof. Euripides G.Μ. Petrakis  
     
  · COMP 211: Data Structures and Algorithms 
Abstract Data Types, implementation in Java, algorithm complexity, performance analysis of algorithms. Sorting in main and external memory, sorting algorithms: bubble sort, exchange sort, insertion sort, selection sort, quick sort, merge sort, k-way merge sort, radix sort. Stacks, queues, linked lists. Implementation of one-dimensional arrays and dynamic memory allocation. Trees, tree traversal, binary search trees, operations research in binary trees (search, insert, delete data). Implementation using arrays and dynamic memory allocation. Applications, Huffman codes. Graphs, graph traversal. Operations on graphs (search, insertion, deletion). Implementation of graphs and applications (minimum spanning tree, shortest path). Searching in main or external memory. Sequential search (binary search, interpolation search, self-adjusting search), Indexed sequential search, ISAM. Performance analysis of search. Hierarchical search trees, trees in main memory (binary search trees, AVL trees, optimal trees, splay trees), analysis of performance. Trees on the secondary memory (multi-way search trees, B-trees, B +-trees), VSAM. Tries, digital search trees, text tries, Patricia tries, Ziv-Lembel coding. Searching in text (KMP, BMH algorithms). Non-hierarchical search, hashing in the main memory, collision resolution, open addressing, separate chaining. Complexity of search. Hashing in external memory (dynamic hashing, extendible hashing, linear hashing). Performance analysis of search.
Instructor: Prof. Euripides G.Μ. Petrakis  
     
  · COMP 411: Theory of Computation 
Sets, relations, alphabets, languages. Finite state automata, regular expressions, regular languages. Equivalence of finite automata and regular expressions. State minimization. Lexical analysis. Pushdown automata, context-free grammar, context-free languages. Equivalence of pushdown automata and context-free grammars. Syntactic parsing. Turing machines and extensions, unrestricted grammars, recursive languages. Non-determinism, non-deterministic Turing machines, recursive enumerable languages. The language hierarchy. Decidability, computability, non-computability. Church-Turing thesis. Universal Turing machines, reductions. Rice's theorem. Computational complexity and complexity classes. Cook's theorem. Application to compiler construction and laboratory instruction of the tools flex, bison, JavaCC.
Instructor: Prof. Michail G. Lagoudakis  
     
  · COMP 417: Artificial Intelligence 
Foundation and history of Artificial Intelligence. Intelligent agents and environments. Systematic search methods: uninformed, informed, heuristic. Local search methods. Constraint satisfaction problems and algorithms. Basic game theory and adversarial search. Propositional logic, first-order logic, reasoning, inference algorithms. Knowledge representation and knowledge bases. Reasoning systems, theorem provers, logic programming. Planning problems and algorithms. Planning in the real world and multi-agent planning.
Instructor: Prof. Michail G. Lagoudakis  
     
  · COMP 503: Autonomous Agents 
Agents and environments, uncertainty and probability, probabilistic reasoning. Bayesian networks, exact and approximate inference in Bayesian networks, enumeration and sampling algorithms. Temporal probabilistic reasoning (filtering, prediction, smoothing, most likely sequence), dynamic Bayesian networks. Mobile robot navigation: motion control, path planning, localization, mapping, simultaneous localization and mapping (SLAM). Decision making under uncertainty, Markov decision processes, optimal policies, value iteration, policy iteration. Reinforcement learning, prediction and control, basic and advanced reinforcement learning algorithms. Multi-agents systems, game theory, multi-agent coordination, coordinated learning. Applications to autonomous robotic agents and laboratory instruction of robot programming tools.
Instructor: Prof. Michail G. Lagoudakis  
     

POSTGRADUATE COURSES
  · AIS 603: Multimedia Data Management 
Processing, archiving, and searching multimedia information including documents, one-dimensional signals, still and moving images (video) in information systems and the Internet. Classic models of information retrieval (binary, relational, probabilistic), information clustering and clustering algorithms (partitional, hierarchical, hybrid algorithms), clustering applications grouping in document collections. Visualization of one-dimensional signals and images in multimedia systems. Feature extraction (color, texture, shape, and spatial relationships) from images. Retrieval methods for onedimensional signals and images. Indexing techniques in information systems for documents and multimedia information (inverted files, k-d rees, grid files, R-trees). Design of information systems on the Internet, management and analysis of information on the Internet (PageRank and HITS methods). Basic processing techniques and analysis of still and moving images (video) in information systems. Compression techniques, JPEG, MPEG-1, 2, 4, 7 standards. Video segmentation into shots, shot aggregates.
Instructor: Prof. Euripides G.Μ. Petrakis  
     
  · COMP 607: Machine Vision 
Basic principles and methodology of machine vision with emphasis on algorithms and applications of machine vision. Image formation, mathematical, geometric, colour, frequentist, discrete models. Basic image processing techniques (filtering, enhancement, normalization). Edge detection, first and second derivative operators. Image segmentation, methods for segmenting or enhancing regions and edges, thresholding techniques. Advanced segmentation techniques (merging and splitting regions and edges, relaxed ordering, Hough technique). Techniques for processing binary images, distance transforms, morphological operators, and region labeling. Analysis, representation, and recognition of images. Representation of edges and regions, representation and recognition of shapes, representation and recognition of structural content. Texture analysis and recognition, structural and statistical methods. Dynamic vision, estimation of motion, optical flow, and trajectory.
Instructor: Prof. Euripides G.Μ. Petrakis  
     
  · COMP 613: Machine Learning 
Basic concepts of machine learning and statistics. Supervised learning: least mean squares (LMS), logistic regression, perceptron, Gaussian discriminant analysis, naive Bayes, support vector machines, model selection and feature selection, ensemble methods (bagging, boosting). Learning theory: bias/variance tradeoff, union and Chernoff/Hoeffding bounds, VC dimension. Unsupervised learning: clustering, k-means, EM, mixture of Gaussians, factor analysis, principal components analysis (PCA), independent components analysis (ICA). Reinforcement learning: Markov decision processes (MDPs), Bellman equations, value iteration, policy iteration, value function and policy approximation, least-squares methods, reinforcement learning algorithms, partially observable MDPs (POMDPs), algorithms for POMDPs.
Instructor: Prof. Michail G. Lagoudakis  
     
  · COMP 625: Autonomous Agents 
Agents and environments, uncertainty and probability, probabilistic reasoning. Bayesian networks, exact and approximate inference in Bayesian networks, enumeration and sampling algorithms. Temporal probabilistic reasoning (filtering, prediction, smoothing, most likely sequence), dynamic Bayesian networks. Mobile robot navigation: motion control, path planning, localization, mapping, simultaneous localization and mapping (SLAM). Decision making under uncertainty, Markov decision processes, optimal policies, value iteration, policy iteration. Reinforcement learning, prediction and control, basic and advanced reinforcement learning algorithms. Multi-agents systems, game theory, multi-agent coordination, coordinated learning. Applications to autonomous robotic agents and laboratory instruction of robot programming tools.
Instructor: Prof. Michail G. Lagoudakis  
     
 

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