BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//127.0.1.1//NONSGML kigkonsult.se iCalcreator 2.41.92//
CALSCALE:GREGORIAN
METHOD:PUBLISH
UID:62333535-3331-4461-a533-336466376439
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:39326435-6635-4365-a261-313634616334
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231026T140000Z
DTEND:20231026T160000Z
SUMMARY:Lecture 1 - Bias and Variance
END:VEVENT
BEGIN:VEVENT
UID:30346338-3730-4537-a638-633332333865
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231102T150000Z
DTEND:20231102T170000Z
SUMMARY:Lecture 2 - Linear Regression 1
END:VEVENT
BEGIN:VEVENT
UID:64653263-6164-4230-b163-323963636263
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231109T150000Z
DTEND:20231109T170000Z
SUMMARY:Lecture 3 - Linear Regression II
END:VEVENT
BEGIN:VEVENT
UID:66333930-6561-4661-b135-623135306362
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231116T150000Z
DTEND:20231116T170000Z
SUMMARY:Lecture 4 - Classification I
END:VEVENT
BEGIN:VEVENT
UID:35623763-6638-4339-b866-373031616465
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231123T150000Z
DTEND:20231123T170000Z
SUMMARY:Lecture 5 - Classification II
END:VEVENT
BEGIN:VEVENT
UID:63646533-6136-4633-b839-613139353538
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231130T150000Z
DTEND:20231130T170000Z
SUMMARY:Lecture 6 - Generalization
END:VEVENT
BEGIN:VEVENT
UID:66313130-3438-4639-b966-336366316431
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231207T150000Z
DTEND:20231207T170000Z
SUMMARY:Lecture 7 - Model Selection
END:VEVENT
BEGIN:VEVENT
UID:30313237-6534-4530-b234-613537393062
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20231214T150000Z
DTEND:20231214T170000Z
SUMMARY:Lecture 8 - Beyond Linear
END:VEVENT
BEGIN:VEVENT
UID:37313134-6338-4137-a335-303337643935
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20240104T150000Z
DTEND:20240104T170000Z
SUMMARY:Lecture 9 - Dimensionality Reduction
END:VEVENT
BEGIN:VEVENT
UID:32323465-3065-4163-b864-383661326231
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20240111T150000Z
DTEND:20240111T170000Z
SUMMARY:Lecture 10 - Clustering
END:VEVENT
BEGIN:VEVENT
UID:32616137-3039-4535-b932-386434396634
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20240118T150000Z
DTEND:20240118T170000Z
SUMMARY:Lecture 11 - Trees and Forests
END:VEVENT
BEGIN:VEVENT
UID:63386336-3065-4132-b862-323666666432
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20240125T150000Z
DTEND:20240125T170000Z
SUMMARY:Lecture 12 - Support Vector Machines
END:VEVENT
BEGIN:VEVENT
UID:38613764-6366-4464-a537-306261633831
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20240201T150000Z
DTEND:20240201T170000Z
SUMMARY:Lecture 13 - Neural Networks
END:VEVENT
BEGIN:VEVENT
UID:30633864-3339-4239-b737-386365323739
DTSTAMP:20260423T150329Z
DESCRIPTION:
DTSTART:20240208T150000Z
DTEND:20240208T170000Z
SUMMARY:Lecture 14 - ML and the Real World
END:VEVENT
END:VCALENDAR
