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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.

**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

**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

courses/be4m33ssu/start.txt · Last modified: 2020/01/24 20:24 by flachbor