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.

• 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

• 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: WS19/20
  • Lectures: Tuesday 12:45-14:15, 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 example

• Textbooks and References:
  • [1] M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, MIT Press, 2012 [PDF]
  • [2] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Springer, 2010 [PDF]

Lectures

Labs and Seminars