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

Links:
[[courses:be4m33ssu:lectures|Lectures]],
[[courses:be4m33ssu:labs|Labs and Seminars]],
[[https://cw.felk.cvut.cz/forum/forum-1735.html|Student discussion forum]],
[[http://cw.felk.cvut.cz/upload/|upload system]],
[[https://fel.cvut.cz/cz/education/rozvrhy-ng.B211/public/html/predmety/46/84/p4684906.html|course schedule]],
[[https://fel.cvut.cz/cz/education/harmonogram2122.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 =====
  * **17.01.2022** We prepared a page with exam instructions (see below)
  * **24.09.2021** We provide recordings of the lectures (see below).


===== Details =====
  * **Teachers:** [[http://cmp.felk.cvut.cz/~flachbor/ |Boris Flach]], [[http://cmp.felk.cvut.cz/~xfrancv//|Vojtech Franc ]], [[http://cs.felk.cvut.cz/en/people/drchajan|Jan Drchal]] + [[http://mrs.felk.cvut.cz/people/postdocs/daniel-bonilla | Daniel Bonilla ]]

  * **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:** [[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B201/public/html/predmety/46/84/p4684906.html | WS21/22 ]]
    * Lectures: Tuesday 12:45-14:15, [[ http://cyber.felk.cvut.cz/contact/#maps | 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 {{exam_ws16.pdf|example}}

  * **Textbooks and References:**
    * [1] M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012 [[https://pdfs.semanticscholar.org/e923/9469aba4bccf3e36d1c27894721e8dbefc44.pdf|[PDF]]]
     * [2] 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]]]
     * [3] I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016 [[https://www.deeplearningbook.org| [link]]]
     * [4] G. Louppe, Understanding Random Forests: From Theory to Practice, 2014 [[https://arxiv.org/abs/1407.7502| [link]]]


===== Materials =====

[[courses:be4m33ssu:lectures|Lectures]]

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

[[courses:be4m33ssu:recordings:|Lecture recordings]]

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

/* [[courses:be4m33ssu:micro-credentials|Micro-credentials]] */

[[courses:be4m33ssu:exam|Exam WS 21/22]]