====== BE4M33SSU - Statistical Machine Learning ======

Links:
[[courses:be4m33ssu:lectures|Lectures]],
[[courses:be4m33ssu:labs|Labs and Seminars]],
[[https://cw.felk.cvut.cz/forum/forum-1325.html|Student discussion forum]],
[[http://cw.felk.cvut.cz/upload/|upload system]],
[[http://www.fel.cvut.cz/cz/education/rozvrhy-ng/public/html/predmety/46/84/p4684906.html|course schedule]],
[[http://www.fel.cvut.cz/cz/education/harmonogram1718.html|schedule of academic year]].


===== Overview =====
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.

===== News =====
 
  * ** 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**.

===== Details =====
* **Teachers:** [[http://cmp.felk.cvut.cz/~flachbor/ |Boris Flach]], [[http://cmp.felk.cvut.cz/~xfrancv//|Vojtech Franc ]] and [[http://cs.felk.cvut.cz/en/people/drchajan|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:** [[http://www.fel.cvut.cz/cz/education/rozvrhy-ng.B171/public/html/predmety/46/84/p4684906.html|WS17/18]]
  * Lectures: Tuesday 12:45-14:15, [[ http://cyber.felk.cvut.cz/contact/#maps | KN:E-107]], 
  * Labs/Seminars: Thursday 11:00-12:30 and 12:45-14:15, [[ http://cyber.felk.cvut.cz/contact/#maps | 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 {{exam_ws16.pdf|example}}

* **Textbooks and References:**
   * [1] M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012 [[ftp://doc.nit.ac.ir/cee/jazayeri/MachineLearning/Mehryar_Mohr%202012/[Mehryar_Mohri_Afshin_Rostamizadeh_Ameet_Talwalkar(BookFi.org).pdf|[PDF]]]
   * [2] K.P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012 [[https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf|[PDF]]]
   * [3] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010 [[http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf|[PDF]]]
   * [4] R.S. Sutton, A.G. Barto: Reinforcement Learning: An Introduction, 1998 [[http://people.inf.elte.hu/lorincz/Files/RL_2006/SuttonBook.pdf|[PDF]]]

===== Materials =====

[[courses:be4m33ssu:lectures|Lectures]]

[[courses:be4m33ssu:labs|Labs and Seminars]]

/* [[courses:be4m33ssu:evaluation|Evaluation]] */