BE4M33SSU - Statistical Machine Learning


The aim of statistical machine learning is to develop systems (models and algorithms) able to learn to solve prediction tasks given a set of examples and some prior knowledge about the task. Students will gain the ability to construct learning systems for typical applications by successfully combining appropriate models and learning methods.



* Teachers: Boris Flach, Vojtech Franc and Jan Drchal

* Prerequisites:

  • probability theory and statistics comparable to the course A0B01PSI
  • pattern recognition and decision theory comparable to the course AE4B33RPZ
  • linear algebra and optimisitaion comparable to the course AE4B33OPT

* Course format: (2/2)

  • lectures: weekly
  • practical and theoretical labs (tutorials) alternating every second week

* Schedule: WS17/18

  • Lectures: Tuesday 12:45-14:15, KN:E-107,
  • Labs/Seminars: Thursday 11:00-12:30 and 12:45-14:15, KN:E-220,

* Grading/Credits:

  • Thresholds for passing: at least 50% of the regular points in the labs and at least 50% of the regular points in the exam
  • Weights for final grading: 40% practical labs + 60% written exam = 100% (+ bonus points)
  • Credits: 6 CP

* Textbooks and References:

  • M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012 [PDF]
  • K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012 [PDF]
  • T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010 [PDF]
  • R.S. Sutton, A.G. Barto: Reinforcement Learning: An Introduction, 1998 [PDF]


courses/be4m33ssu/start.txt · Last modified: 2017/09/08 17:04 by flachbor