BE4M33SSU - Statistical Machine Learning

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.

• 07.02.19 We propose a third term for the exam. It will take place on Tuesday, Feb, 12 at 12:40-14:15 in KN:G-205. We currently encounter some problems with entering it to KOS, but hope to resolve it soon. You may come to this term in any case.
• 07.02.19 Results of the second term of the exam have been corrected and can be found in KOS. Students, who wish to inspect their results, can come on Monday, Feb. 11 in the afternoon (KN:G-105).
• 28.01.19 Term 1 exam has been corrected. The results are in KOS. The corrected tests can be seen in G105 on Tuesday 29.1 and Thursday 31.1. Those who want to discuss their results please contact by email a teacher who corrected the corresponding assignment (A1,A2-V.Franc, A3-B.Flach, A4,A5-J.Drchal). V.Franc and J.Drchal will be available for test results discussion on Thursday 31.1. at 13:30-14:30 in G105.
• 18.01.19 Exam materials: you are allowed to bring one A4 page (single-sided) with handwritten notes to the exam. We follow a “no electronic gadgets” policy at the exam.
• 03.01.19 Exam dates: 1. term: 22.01.2019 11:30-13:00 KN:E-107, 2. term: 05.02.2019 11:30-13:00 KN:E-107
• 27.11.18 A voluntary mid-term test is available here . If you want it to be corrected, please submit your solutions by Thursday, Dec., 6 (e.g. at the lab). The solution for each assignment should be provided on a separate sheet of paper (Don't forget to write your name on each page you are submitting).
• 19.11.18 The lecture on 20.11 is moved to the lecture hall KN:A215

* 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: WS18/19

• Lectures: Tuesday 12:45-14:15, KN:E-107,
• Labs/Seminars: Thursday ): 9:15-10:45 KN:G205, 11:00-12:30 KN:E-220 and 12:45-14:15 KN:G205,

* 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]

Lectures

Labs and Seminars