Description
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: Euripides G.Μ. Petrakis
Courses Portal Link: Multimedia Data Management

Description
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: Euripides G.Μ. Petrakis
Courses Portal Link: Machine Vision

Description
Complex applications of pointers in the C language. Pointers to pointers. Recursion. Introduction to Java and abstraction in object–oriented programming. The notion of a class and an object. Input/output, parameter passing in methods, access levels of member variables/methods/classes, overloading, inheritance, polymorphism, abstract classes. Abstract data types. Examples of abstract data types. Lists and their versions (single/double linked lists, circular lists). Queues and stacks. Divide and conquer strategies. Binary search trees. Hash–based structures. Simple sorting and search algorithms.
Instructor: Michail G. Lagoudakis
Courses Portal Link: Structured Programming

Description
System Lifecycle/Development Methodologies. Object-Oriented Design and Development. Requirements capturing. Project Feasibility Study. System Analysis and System Design. UML and main types of UML diagrams (use-case, class, sequence, collaboration, state machines). User Interface Design basics. Java: interfaces, threads, exceptions, files, event processing. Analysis and Design Patterns.
Instructor: Georgios Chalkiadakis
Courses Portal Link: Design and Development of Information Systems

Description
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: Euripides G.Μ. Petrakis
Courses Portal Link: Data Structures and Algorithms

Description
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: Michail G. Lagoudakis
Courses Portal Link: Theory of Computation

Description
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: Michail G. Lagoudakis
Courses Portal Link: Artificial Intelligence

Description
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, partial observability. Reinforcement learning, prediction and control, basic and advanced reinforcement learning algorithms. Approximation methods for multi-dimensional and continuous spaces. Competitive agents, planning and learning in Markov games. Auction-based multi-agent coordination. Applications to autonomous robotic agents and laboratory instruction of robot programming tools.
Instructor: Michail G. Lagoudakis
Courses Portal Link: Autonomous Agents

Description
Agent types and characteristics. Multiagent systems and agent interactions. Links to Game Theory and Artificial Intelligence. Focus on agents that are rational utility maximizers. Decision making using utility theory, decision theory and game theory. Preferences, utility functions, utility maximization and rationality. Taking strategic decisions. One-shot and repeated strategic games. Nash equilibirum, Pareto optimality, and other game theoretic solution concepts. Equilibrium selection. Distributed problem solving. Coalitional games and coalition formation. Coalition formation applications (e-commerce, telecommunication networks, decentralized electricity market and the smart electricity grid). Trust and reputation. Bargaining and negotiations. Electronic auctions. Auctions and mechanism design. Auction and mechanism design applications (electronic auctions, ad auctions). Opponent modelling and learning in games. Connections to Machine Learning. Handling uncertainty. Multiagent systems' applications: agents in telecommunication/ad-hoc wireless/peer-to-peer networks, sensor networks, the smart electricity grid.
Instructor: Georgios Chalkiadakis
Courses Portal Link: Multiagent Systems

Description
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: Euripides G.Μ. Petrakis
Courses Portal Link: Multimedia Data Management

Description
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: Michail G. Lagoudakis
Courses Portal Link: Machine Learning

Description
Utility Theory, Decision Theory, and Game Theory (cooperative and non-cooperative). Rationality and strategic decision making. Reinforcement Learning and Multiagent Reinforcement Learning. Unsupervised Learning and Probabilistic Topic Modeling. Deep Learning and Deep Reinforcement Learning). Learning in Game-Theoretic Settings.
Instructor: Georgios Chalkiadakis
Courses Portal Link: Decision Making and Learning in Multi-Agent Worlds

Description
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: Euripides G.Μ. Petrakis
Courses Portal Link: Machine Vision

Description
Uncertainty and probabilistic reasoning. Robotics perception and action. Recursive state estimation: state space, belief space, prediction and correction, Bayes filter. Estimation filters: linear Kalman filter, extended Kalman filter, unscented Kalman filter, histogram filter, particle filter. Probabilistic motion models: velocity model, odometry model, sampling and density. Probabilistic observation models: beam model, scan model, feature model, sampling and density. Robot localization: Markov, Gaussian, Grid, Monte-Carlo. Robotic mapping: occupancy grid maps, feature maps, simultaneous localization and mapping (SLAM). Decision making under uncertainty, Markov decision processes, optimal policies, value iteration, policy iteration, partial observability. Reinforcement learning, prediction and control, basic and advanced reinforcement learning algorithms. Multi-robot coordination and learning.
Instructor: Michail G. Lagoudakis
Courses Portal Link: Probabilistic Robotics