FR 27.3.2020 - Short Course Machine Learning

[Sanahuja Solange]

Hallo liebe Madlene,


das ist super cool! Ich weiss immer noch nciht mit R programmieren und versuche bis dahin ein bischen reinzuschauen.

Aber auch allgemein verstehen, was es im AI Neues gibt und Techniken die ich noch nicht benutzt habe, ist eine tolle Möglichkeit!


Ich danke dir, dass du solche Austausche ermöglichst,


LG

Solange

________________________________
De : bfh-r-users <bfh-r-users-bounces at lists.bfh.science> de la part de Madlene Nussbaum <madlene.nussbaum at bfh.ch>
Envoyé : vendredi, 28 février 2020 18:07:17
À : bfh-r-users at lists.bfh.science
Objet : FR 27.3.2020 - Short Course Machine Learning

<English below>


Liebe alle

Viele haben sich im Doodle eingetragen ;-)
Der ML-Kurs findet statt am:

FR 27.3.2020 - 10:30-17:00
Wo: A.1.05, HAFL Zollikofen

Dieser Kurs ersetzt das R User Treffen vom 11.3. Ich sage dieses hiermit ab.

Schönes Wochenende
Madlene

-----

Dear all

Many people filled in the Doodle ;-)
The ML course is taking place on

FR 27.3.2020 - 10:30-17:00
Where: A.1.05, HAFL Zollikofen

The course replaces the R User meeting of 11.3. This meeting is canceled.

Take care
Madlene



======= Program =======

**Short Course: Mastering Machine Learning for Prediction**

Time: 10:30 - 17:00 Location: presumably HAFL, Zollikofen

Language lecture: English if requested, else German.
Language materials: English.

Expected previous knowledge: Basic know-how of simple and multiple linear regression
(ordinary least squares-method).
e.g. catch up by watching these very simple explanatory videos (1 hour on speed 1.25):
https://www.youtube.com/watch?v=Qa2APhWjQPc
https://www.youtube.com/watch?v=iAgYLRy7e20
https://www.youtube.com/watch?v=dQNpSa-bq4M

Please feel free to just join for a part of the course.

Suggested Schedule
------------------------------

Morning: Repeat linear regression if needed (self-study)

10:30 Part I: Introduction and Overview of Machine Learning Methods for Prediction,
Overfitting. Methods in the Context of Supervised Learning: Lasso, Support Vector
Machines, Random Forest, Boosting, Model averaging.

12:00 Lunch Break (Mensa)

13:00 Practical training, Exercises Part I

14:00 Part II: Model Selection and Model Building, Model Interpretation, Prediction
Intervals, Uncertainty, parametric and non-parametric Bootstrap

15:00 Coffee Break

15:20 Practical training, Exercises Part II

16:20 Part III: Model Evaluation, Cross-Validation, Validation Statistics, Good Practice
and Interpretation, R2 vs. r2, Scores for Classification.
-----



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