Κατεύθυνση: Major in Communications & Networks

Information Retrieval

Learning Outcomes

The aim of this course is learning fundamental concepts of information retrieval systems. The course’s contents cover all stages of system design and implementation for collection, indexing and searching of text documents, as well as evaluation methods. In addition, recent trends in information retrieval are also covered, for example information retrieval from the WWW.

Upon successful completion of the course, the students will be in position:

  • to know representation models for text documents.
  • to use techniques for indexing, compression, retrieval and scoring of documents.
  • to develop applications that manage large volumes of text.
  • to build the functionality of a search engine.
  • to apply machine learning techniques for text classification.

Course Contents

  • Introduction and basic IR concepts
  • System architecture of IR systems
  • Dictionaries and inverted indexes
  • Construction and compression of dictionaries
  • Information retrieval models (boolean model, vector space model, probability models)
  • Scoring and ranking documents
  • Language models
  • Information retrieval from XML documents
  • Basic concepts of information retrieval from the WWW
  • Web crawling and indexing
  • Text classification with machine learning techniques, support vector machines, algorithms for text classification

Recommended Readings

  • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
  • Ricardo A. Baeza-Yates and Berthier Ribeiro-Neto. 1999. Modern Information Retrieval. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA.

Pattern Recognition

Learning Outcomes

Pattern recognition is the scientific field that deals with the assignment of a label to a given input value. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes. The course aims to cover the most popular in the literature techniques for pattern recognition, as they are typically employed in a number of practical applications, such as speech and audio recognition, image and video analysis, biometrics and bioinformatics. The course covers the most commonly used classification algorithms, feature selection techniques, data transformation methods, and data clustering.

Students, upon successful completion of the course, will be able to:

A) Understand the key standards recognition methodologies

B) Analyze problems in various areas of application, such as voice and audio recognition, image and video analysis, biometrics and bioinformatics.

C) Choose the best classifiers, feature selection methods, data transformations, and clustering.

D) Evaluate standard pattern recognition systems

 

Course Contents

  • Introduction to Pattern recognition systems
  • Parametric estimation of probability density function (maximum Likelihood estimation, maximum a posteriori
  • Bayesian classifiers and Bayesian Networks
  • k-nearest neighbor
  • Non parametric estimation of probability density function (Parzen windows)
  • Linear classifiers, non linear classifiers. Perceptron algorithm. Multilayer neural networks, Deep Learning
  • Unsupervised Pattern recognition – Clustering
  • Feature generation: contour representation and contour tracing, chain code, polygon, signatures, linear transforms, Fourier Transform, regional features, image recognition, bias and variance, texture
  • Feature Selection and Kernels
  • Pattern recognition tools

Recommended Readings

  • Sergios Theodoridis and Konstantinos Koutroumbas. 2008. Pattern Recognition, Fourth Edition (4th ed.). Academic Press

Algorithms for Electronic Markets

Learning Outcomes

The course’s material includes the theory and practice that pertain to the design of economic mechanisms for automated trade exchanges, in modern digital platforms (auction websites, services provision and products retail websites, internet advertisement platforms). In particular, the course concerns the modern algorithmic techniques that facilitate the digital implementation of electronic markets.

Upon successful completion of the course, the students will be in position:

  • to understand the economic and algorithmic background that underlies the functionality of electronic markets.
  • to design electronic trade exchanges platforms, by choosing the appropriate economic mechanisms and the relevant algorithmic implementations.
  • to assess and evaluate the performance of economic mechanisms and their algorithmic implementations, relative to a given electronic market and its particulars.
  • to design, implement and evaluate automated pricing mechanisms.

Course Contents

  • Introduction to Game Theory: Strategies, Utility Functions
  • Strategic Games and Nash Equilibrium
  • Efficiency of Equilibria
  • Oligopoly Models
  • Auctions: First-Price, Second-Price, Multi-Unit Formats
  • Algorithmic Mechanism Design
  •  Sponsored Search Auctions
  • Combinatorial Auctions
  • Principles and Methods of Pricing
  • Prediction Techniques
  • Online Auctions

Recommended Readings

  • N. Nisan, T. Roughgarden, E. Tardos, V. Vazirani. Algorithmic Game Theory. Cambridge University Press, 2006.
  • T. Roughgarden. Twenty Lectures on Algorithmic Game Theory. Cambridge University Press, 2016.
  • M. J. Osborne. An Introduction to Game Theory. Oxford University Press, 2009.
  • R. Gibbons. A Primer in Game Theory. Financial Times / Prentice Hall, 1992.