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.

* **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

* **Textbooks and References:**

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