Syllabus

Titel
1909 Exploiting Data: The Machine Learning Approach
LV-Leiter/innen
Mgr. Jan Valendin, Stefan Vamosi, MSc.
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
19.09.2019 bis 26.09.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Termine
Wochentag Datum Uhrzeit Raum
Dienstag 15.10.2019 17:00 - 20:00 TC.4.02
Dienstag 22.10.2019 17:00 - 20:00 TC.4.02
Dienstag 29.10.2019 17:00 - 20:00 TC.4.02
Dienstag 05.11.2019 17:00 - 20:00 TC.4.02
Dienstag 12.11.2019 17:00 - 20:00 TC.4.02
Dienstag 19.11.2019 17:00 - 20:00 TC.4.02
Dienstag 03.12.2019 17:00 - 20:00 TC.4.02
Dienstag 10.12.2019 17:00 - 19:00 TC.4.02

Inhalte der LV

The fast rise of available data for customer and market analysis creates an immense opportunity for machine learning applications. For predictive tasks, these models often outperform conventional statistical approaches. This course introduces some of the modern machine learning techniques including the basic theoretical background with hands-on exercises and group works.

The course includes a short introduction to R, supervised machine learning, model performance evaluation, cross-validation and generalization. We will focus on the application and the concept of the following machine learning methods: k-nearest neighbors (kNN), decision trees, random forests, k-means clustering, neural networks and support vector machines (SVM).

Lernergebnisse (Learning Outcomes)

The learning objectives of this course are as follows:

  • Understand the basic theoretical background of various supervised machine learning algorithms.
  • Get familiar with various machine learning algorithms in R by exercising on real data examples.
  • Gain the knowledge to pick the right machine learning method for a particular problem.

Regelung zur Anwesenheit

You need to attend at least 80% of all classes to pass the course.

Lehr-/Lerndesign

This course uses various teaching methods like classic lecture format, guided computer exercises, group works and short student presentations.

Leistung(en) für eine Beurteilung

The final grade will be evaluated as follows:

  • Class participation: [weight: 10%]
  • Individual computer exercises (5 homeworks): [weight: 50%]
  • Final exam (open questions and multiple choice): [weight: 40%]
  • Group challenge: [weight: 10% Bonus in Total]

For the individual home exercises, a random draw will select students to make a short presentation about their findings. If a presentation has failed, the final grade will be reduced by one grade.

To pass this course, your weighted final grade needs to exceed 60%.

Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

The class will not take place in a computer room. Please bring your own Notebook with an R-Studio installation to attend the class. In exceptional cases, a Notebook can be provided by the institute.

The software is open source and can be downloaded from: www.rstudio.com

Literatur

1 Autor/in: Brett Lantz
Titel:

Machine Learning with R


Verlag: Packt Publishing
Auflage: 2
Jahr: 2015
Empfehlung: Referenzliteratur
Art: Buch

Empfohlene inhaltliche Vorkenntnisse

Basic knowledge of standard statistical software packages, such as R (or Python) are recommended. For those who are not familiar with R and to ensure a shared set of R skills among all participants, we will provide a condensed introduction to R on the first day and an online exercise.

Zuletzt bearbeitet: 26.08.2019



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