198842 VU Data Analysis II: Machine Learning for Data Analysis

summer semester 2020 | Last update: 23.06.2020 Place course on memo list
VU Data Analysis II: Machine Learning for Data Analysis
VU 3

How can we design learning software systems that adjust their parameters according to example data, continually optimize their own performance, and/or automatically adapt to changing contexts? This course conveys knowledge of basic techniques and competences in the formulation and solution of problems of machine learning.

Fundamentals in statistical methods. Suprevised learning: classification and regression. Unsupervised learning: clustering, density estimation, and dimensionality redection.

The lecture covers theoretical material that the complementary proseminar exercises in discussions, written problems, and programming projects.

Course assessment is based on regular written and/or oral contribution by participants.

This course will take most material from Pattern Recognition and Machine Learning by Chris Bishop.

Additional material will be taken from other sources and will be provided to the students before the lecture.

Basic Python programming skills are requried in this course.

It is recommened to take this course after completion of "VU Introduction to Programming: Programming in Python" from the Complementary Subject Area Digital Science or an equivalent course.

The acceptance is based on prioritised randomisation. Students who passed the above mentioned course get precedence. 

not applicable

This course is provided as a combinatin of 703.075 and 703.076 VO+PS Machine Learning:

This course is identical to Machine Learning 703075 (VO3) + 703076 (PS2), minus the Reinforcement Learning content. Students will attend these VO and PS for the first 9 weeks of class.

Slots: see 703.075 and 703.076-4 (group  4)

siehe unter "Anmerkungen" / see "Remarks",