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Links: Lectures, Labs and Seminars, Student discussion forum, upload system, course schedule, schedule of academic year.

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