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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.


  • 10.02.20 The third and last term of the exam is scheduled for Thu, 13.2, 9:15 in KN:G-205 and is open for enrolling.
  • 24.01.20 The results of the first term of the exam are in KOS. You are welcome to come on Tue, 28.1. starting from 12:45pm to get insight to the correction details (room KN:G-105).
  • 13.01.20 The duration of the written exam is 90 minutes. You are allowed to prepare & use one A4 page with handwritten notes (one sided). We do not supply paper. Please bring enough paper for writing & submitting your solutions (at least 1 sheet per assignment, for ca. 5-6 assignments).
  • 19.12.19 Dear Students, the reserve lecture on January, 7 will be dedicated to consultations. All teachers will be available for you at their respective offices. You are of course invited to come for consultations on other dates by appointment.
  • 12.12.19 Exam terms: term 1: 20.01.20, 14:00pm, KN:E-107 term 2: 03.02.20, 14:00pm, KN:E-107
  • 18.11.19 The lecture on Tue. 19.11. will be relocated to the lecture hall KN:A-215


  • 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: WS19/20
    • 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 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: 2020/02/10 19:17 by flachbor