#Content

Machine Learning avanciert zum elementaren Wertschöpfungs- und Erfolgsfaktor der Digitalisierung. Dieser Workshop vermittelt einen Überblick über die Möglichkeiten, Methoden und Einsatzgebiete von Machine Learning. Dieser Kickstart setzt sich aus den beiden Bereichen Supervised und Unsupervised Machine Learning zusammen.

Zu den Themen gehören dabei Klassifizierungsverfahren, Bayes Klassifikatoren, lineare Klassifikatoren, Entscheidungsbäume und Nächste-Nachbarn Klassifikatoren. Dabei werden nicht nur die technischen Seiten der Methoden, sondern auch die jeweiligen Vor-und Nachteile erläutert sowie beispielhafte Anwendungsfälle aufgezeigt. Abschließend wird noch ein Einblick in Neuronale Netze und Deep Learning gewährt. Der Workshop wird durchgehend durch Praxisübungen begleitet.

Nach der Absolvierung des Workshops sind die Teilnehmer mit modernen Supervised und Unsupervised Machine Learning Ansätzen vertraut.

Unsere erfahrenen Dozenten verfügen über mehrjährige Berufserfahrung in verschiedenen Branchen. Dadurch gewähren wir einen hohen Praxisbezug unserer Schulungen und Trainings.

#KeyTakeaways

  • Insight into different relevant methods of Machine Learning

  • Knowledge of advantages and disadvantages for certain applications

  • Kickstart to use Machine Learning in your daily work routine

#Requirements

Basic knowledge in statistics, mathematics and computer science are necessary.

#KeyFacts

  • Location: Meetinn – Munic
  • Language: Englisch
  • max. participants: 15
  • Time: 10:00 a.m. – 5:30 p.m.

Sind Sie an diesem Workshop interessiert?

#Agenda

    1. Introduction to Machine Learning and why we need it: descriptive analytics vs. predictive analytics
    2. Classification of Machine Learning in the Business Context: Application Examples
    3. Clarification of the most important basic concepts and definition of the problems in Supervised Learning
    4. Bayesian classifiers
      • Main principle of Bayesian classifiers (theorem of Bayes, Baye’s decision rule)
      • Example implementation of the basic idea: naive Bayes classifiers
      • Discussion of the advantages and disadvantages on the basis of exemplary applications
    5. Linear classifiers
      • Functionality of linear classifiers
      • Illustration of the basic idea: Support Vector Machines (SVMs)
      • Extension of SVMs to non-linear classification (Kernel Machines)
      • Discussion of the advantages and disadvantages on the basis of possible use cases
    6. Decision trees
      • Basic principle of decision trees
      • Example implementation of the basic idea: discussion of different split strategies
      • Extension of decision trees: Pruning to avoid overfitting
      • Discussion of the advantages and disadvantages with the help of real-life use cases
    7. Next neighbours (NN) classifiers:
      • Basics of NN classifiers
      • Example implementation of the basic idea: discussion of different weightings and voting strategies
      • Discussion of the advantages and disadvantages of the application using the example of use cases
      • Evaluation of the quality of learned models (classifiers)
      • Discussion of the challenges, in particular of overfitting
      • Overview of measures against overfitting: the train-and-test paradigm
    8. Basic introduction to the concepts of neural networks and Deep Learning
    9. Introduction to Data Mining and Unsupervised Learning and why we need it: labeled vs. unlabeled Data
    10. Classification of Unsupervised Learning in the Business Context: Application Examples

Trainer

Steffen Bunzel

Steffen Bunzel has been with Alexander Thamm GmbH for 3 years. His focus is on the development of scalable machine learning systems for customers in the telecommunications, automotive and retail industries. Steffen Bunzel is a passionate Pythonista and a fan of Python’s rich data science ecosystem. He loves to experiment with new algorithms, but places great emphasis on developing solutions that are productive in the long run. From his daily work, he knows how difficult it can be to make the jump from prototype to production. Steffen has a background in statistics and operations research and studied industrial engineering in Berlin and London.

Steffen Bunzel - Speaker Data Festival

#Location

Thank you meetin for …

Please note, our DATA festival on the 18th and 19th of March will take place in the Muffatwerk!