BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//127.0.1.1//NONSGML kigkonsult.se iCalcreator 2.41.92//
CALSCALE:GREGORIAN
METHOD:PUBLISH
UID:31653638-3638-4061-a239-383765363031
X-WR-CALNAME:Elements of Machine Learning Calendar
X-WR-CALDESC:Events of the lecture Elements of Machine Learning
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VEVENT
UID:65336132-6537-4330-a332-303336646463
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231026T140000Z
DTEND:20231026T160000Z
SUMMARY:Lecture 1 - Bias and Variance
END:VEVENT
BEGIN:VEVENT
UID:38643063-6136-4138-b162-363331653463
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231102T150000Z
DTEND:20231102T170000Z
SUMMARY:Lecture 2 - Linear Regression 1
END:VEVENT
BEGIN:VEVENT
UID:38343035-6137-4932-b362-303166323633
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231109T150000Z
DTEND:20231109T170000Z
SUMMARY:Lecture 3 - Linear Regression II
END:VEVENT
BEGIN:VEVENT
UID:62313164-3932-4131-b831-633336313334
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231116T150000Z
DTEND:20231116T170000Z
SUMMARY:Lecture 4 - Classification I
END:VEVENT
BEGIN:VEVENT
UID:36346132-3563-4664-a261-386138656230
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231123T150000Z
DTEND:20231123T170000Z
SUMMARY:Lecture 5 - Classification II
END:VEVENT
BEGIN:VEVENT
UID:34393165-6636-4762-b535-393237646239
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231130T150000Z
DTEND:20231130T170000Z
SUMMARY:Lecture 6 - Generalization
END:VEVENT
BEGIN:VEVENT
UID:63616162-6139-4264-b966-366630376265
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231207T150000Z
DTEND:20231207T170000Z
SUMMARY:Lecture 7 - Model Selection
END:VEVENT
BEGIN:VEVENT
UID:65316361-3433-4134-b963-653537613831
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20231214T150000Z
DTEND:20231214T170000Z
SUMMARY:Lecture 8 - Beyond Linear
END:VEVENT
BEGIN:VEVENT
UID:35326366-3539-4439-b464-656465633435
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20240104T150000Z
DTEND:20240104T170000Z
SUMMARY:Lecture 9 - Dimensionality Reduction
END:VEVENT
BEGIN:VEVENT
UID:30313462-6166-4436-a137-643234386261
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20240111T150000Z
DTEND:20240111T170000Z
SUMMARY:Lecture 10 - Clustering
END:VEVENT
BEGIN:VEVENT
UID:35383831-6166-4331-a362-346331663931
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20240118T150000Z
DTEND:20240118T170000Z
SUMMARY:Lecture 11 - Trees and Forests
END:VEVENT
BEGIN:VEVENT
UID:62393038-3234-4532-a363-646336623132
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20240125T150000Z
DTEND:20240125T170000Z
SUMMARY:Lecture 12 - Support Vector Machines
END:VEVENT
BEGIN:VEVENT
UID:63613362-6663-4164-b363-306238343965
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20240201T150000Z
DTEND:20240201T170000Z
SUMMARY:Lecture 13 - Neural Networks
END:VEVENT
BEGIN:VEVENT
UID:30333261-3639-4736-b162-613937666263
DTSTAMP:20261015T135035Z
DESCRIPTION:
DTSTART:20240208T150000Z
DTEND:20240208T170000Z
SUMMARY:Lecture 14 - ML and the Real World
END:VEVENT
END:VCALENDAR
