====== 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]],
[[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B191/public/html/ucitele/-/-/373936.html|course schedule]],
[[http://www.fel.cvut.cz/en/education/calendar.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 =====
  * **10.02.20** The third and last term of the exam is scheduled for Thu, 13.2, 9:15 in KN:G-205 and is open for enrolling.
  * **24.01.20** The results of the first term of the exam are in KOS. You are welcome to come on Tue, 28.1. starting from 12:45pm to get insight to the correction details (room KN:G-105).
  * **13.01.20** The duration of the written exam is 90 minutes. You are allowed to prepare & use one A4 page with handwritten notes (one sided). We do not supply paper. Please bring enough paper for writing & submitting your solutions (at least 1 sheet per assignment, for ca. 5-6 assignments).
  * **19.12.19** Dear Students, the reserve lecture on January, 7 will be dedicated to consultations. All teachers will be available for you at their respective offices. You are of course invited to come for consultations on other dates by appointment.
  * **12.12.19** Exam terms: **term 1:** 20.01.20, 14:00pm, KN:E-107  **term 2:** 03.02.20, 14:00pm, KN:E-107
  * **18.11.19** The lecture on Tue. 19.11. will be relocated to the lecture hall KN:A-215




===== 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:** [[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B191/public/html/predmety/46/84/p4684906.html | WS19/20 ]]
    * 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 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]]]


===== Materials =====

[[courses:be4m33ssu:lectures|Lectures]]

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

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