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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.


  • 17.01.2022 We prepared a page with exam instructions (see below)
  • 24.09.2021 We provide recordings of the lectures (see below).


  • 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
    • alternating practical and theoretical labs (tutorials)
  • Schedule: WS21/22
    • Lectures: Tuesday 12:45-14:15, KN:E-107
    • Labs/Seminars: Thursday 9:15-10:45, 11:00-12:30 and 12:45-14:15, all in KN:E-112
  • Grading/Credits:
    • Thresholds for passing: at least 50% of the regular points in the practical 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]
    • [3] I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016 [link]
    • [4] G. Louppe, Understanding Random Forests: From Theory to Practice, 2014 [link]


courses/be4m33ssu/start.txt · Last modified: 2022/01/17 13:34 by flachbor