Academic Year 2018/2019 - 3° Year - Curriculum A
Teaching Staff: Giovanni Maria FARINELLA
Credit Value: 6
Scientific field: INF/01 - Informatics
Taught classes: 24 hours
Exercise: 24 hours
Term / Semester:

Learning Objectives

The course aims to present theories and techniques for the analysis of multimedia social data ( images , text, tags , metadata ).

Course Structure

Frontal Lessons and Laboratory

Detailed Course Content

  • Social Media, Computational Social Science e Marketing Digitale
  • Big Data, Sentiment Analysis e Visual Analytics
  • APIs and libraries for the exploration, visualization and analysis of social media data
  • Machine Learning e Pattern Recognition algorithms with applications in the context of Social Media
  • Computer Vision algorithms with applications in the context of Social Media
  • Deep Learning

Textbook Information

  1. R. Zafarani, M. A. Abbasi, H. Liu, Social Media Mining - An Introduction, Cambridge University Press, 2014
  2. J. Leskovec, A. Rajaraman, J. D. Ullman, Mining of Massive Datasets, Cambridge Press, 2011
  3. C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006
  4. E. Alpaydin, Introduction to Machine Learning, The MIT Press, 2009
  5. Y. Bengio, I. J. Goodfellow, A.Courville, Deep Learning, Book in preparation for MIT Press, 2015
  6. Duda, P. E. Hart, D. G. Stork, Pattern Classification (2nd ed.), Wiley, 2000
  7. Murphy, Machine Learning – A Probabilistic Perspective, The MIT Press, 2012
  8. R. C. Gonzales, R.E. Woods, Elaborazione delle Immagini Digitali, Pearson Italia 2008
  9. R. Szeliski, Computer Vision: Algorithms and Application, Springer 2010
  10. S. J.D. Prince, Computer Vision: Models, Learning, and Inference, Cambridge University Press, 2012