BE4M33SSU - Statistical Machine Learning


The aim of statistical machine learning is to develop systems (statistical models and algorithms) for learning 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.



  • 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
    • tutorial classes: weekly (alternating practical and theoretical labs)
  • Schedule: WS23/24
    • 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
  • 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 ยท Last modified: 2023/09/26 10:39 by flachbor