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Michail G. Lagoudakis
Position: Associate Professor
Office: 145.A35
Phone: +30-28210-37244
Fax: +30-28210-37542
Email: lagoudakis   at intelligence.tuc.gr
Personal Page: http://www.lagoudakis.gr

Short CV

Michail G. Lagoudakis is an associate professor with the School of Electrical and Computer Engineering of the Technical University of Crete since September of 2005. Prior to this appointment, he held a postdoctoral fellow position at the Georgia Institute of Technology (September 2003 - June 2005), and a visiting researcher position at the Shannon Laboratory of AT&T Labs (May 2000 - August 2000). He received his doctoral degree in Computer Science with distinction from Duke University in May of 2003.

Education

Research Interests

Teaching

    Undergraduate Courses

    • COMP 102: Structured Programming
      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.
      Courses Portal Link: Structured Programming
    • COMP 402: Theory of Computation
      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.
      Courses Portal Link: Theory of Computation
    • COMP 417: Artificial Intelligence
      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.
      Courses Portal Link: Artificial Intelligence
    • COMP 513: Autonomous Agents
      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.
      Courses Portal Link: Autonomous Agents

    Postgraduate Courses

    • COMP 604: Machine Learning
      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.
      Courses Portal Link: Machine Learning
    • COMP 614: Probabilistic Robotics
      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.
      Courses Portal Link: Probabilistic Robotics

Supervision

Publications

Funding

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