198842 VU Data Analysis II: Machine Learning for Data Analysis

summer semester 2021 | Last update: 04.03.2021 Place course on memo list
Dr.-Ing. Matteo Saveriano Dr.-Ing. Matteo Saveriano, +43 512 507 53433, +43 512 507 39753
198842
VU Data Analysis II: Machine Learning for Data Analysis
VU 3
5
weekly
annually
English

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 procedure is based on prioritised randomisation. Students advanced in completion of the Digital Science minor get precedence, especialy these who passed module 1 (Python). 

The course is blocked over 9 first weeks of the semester, therefore it is 5 hours weekly! The lecture part is within 703.075. The content of lectures in both courses is identical up to week 9. In 703.075, additionally Reinforcement Learning is covered. 

see dates
Group 0
Date Time Location
Tue 2021-03-02
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-03-03
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-03-09
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-03-10
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-03-16
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-03-17
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-03-23
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-03-24
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-04-13
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-04-14
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-04-20
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-04-21
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-04-27
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-04-28
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-05-04
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-05-05
09.15 - 11.00 eLecture - online eLecture - online
Tue 2021-05-11
09.15 - 12.00 eLecture - online eLecture - online
Wed 2021-05-12
09.15 - 11.00 eLecture - online eLecture - online
Group Booking period Date of exam
198842-0 2021-08-23 00:00 - 2021-09-13 23:59
2021-09-27
09:00
Saveriano M.