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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.


  • 10.01.2021 All three terms of the exam will take place online. Instructions can be found here.
  • 21.09.2020 Unfortunately, the city of Prague has flagged corona status “red” last Friday. This means to our deep regret that teaching at universities has to switch to fully online mode until further notice. The lectures/seminars/labs of the course will take place as scheduled in the faculty timetables. We will use the BigBlueButton platform (BBB), which runs safely and securely on local university servers. All you need for joining the virtual teaching rooms is a web-browser and a headset. If you are not familiar with this platform, please have a look at https://bigbluebutton.org/ and the demo videos provided there. We will schedule the meetings to start a few minutes ahead of the regular time, so that you can set up your gear and join them without hurry. You will receive invitation e-mails well before each scheduled meeting. The BBB platform is integrated in the faculty upload system BRUTE. Therefore you can also join the meetings via BRUTE.


  • Prerequisites:
    • probability theory and statistics comparable to the course A0B01PSI
    • pattern recognition and decision theory comparable to the course AE4B33RPZ
    • linear algebra and optimisation comparable to the course AE4B33OPT
  • Course format: (2/2)
    • lectures: weekly
    • practical and theoretical labs (tutorials) alternating every second week
  • Schedule: WS20/21
    • Lectures: Tuesday 12:45-14:15, KN:E-107, (online until further notice)
    • Labs/Seminars: Thursday 9:15-10:45, 11:00-12:30 and 12:45-14:15, all in KN:E-112, (online until further notice)
  • 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
    • Exam assignments example
  • Textbooks and References:
    • [1] M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012 [PDF]
    • [2] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010 [PDF]


courses/be4m33ssu/start.txt · Last modified: 2021/01/12 12:12 by flachbor