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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.

**20.12.17**Exam dates: i)**23.01.18**at 11:00-12:30 and ii)**06.02.18**at 11:00-12:30 both taking place in**KN:E-107**.

* **Teachers:** Boris Flach, Vojtech Franc and Jan Drchal

* **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:** WS17/18

- Lectures: Tuesday 12:45-14:15, KN:E-107,
- Labs/Seminars: Thursday 11:00-12:30 and 12:45-14:15, KN:E-220,

* **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] K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012 [PDF]
- [3] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010 [PDF]
- [4] R.S. Sutton, A.G. Barto: Reinforcement Learning: An Introduction, 1998 [PDF]

courses/be4m33ssu/start.txt · Last modified: 2017/12/20 17:30 by flachbor