Seminars referring to Machine Learning

The goal of machine learning, a subfield of artificial intelligence, is not the explicit programming of the computer, but instead enabling it to learn independently from existing data.

The Machine Learning lecture at FAU provides insight into fundamental optimization processes, state-of-the-art machine learning approaches, and Monte Carlo methods. In addition, the associated Machine Learning seminar gives students an overview of various machine learning algorithms.

For an industry perspective, the industry lab Localization and Machine Learning is offered. The lab focuses on both the comprehensive implementation of machine learning projects and on examples in the areas of logistics, automotive applications, virtual reality, and localization that are relevant to practical situations.

The Machine Learning Forum event is a network platform that connects research and industry. In the future, it will take place twice a year. In addition to specialized lectures held by the university and practical lectures from industry experts, the event will stimulate the contribution of ideas and subjects from small and midsize businesses, job placement for graduates, and the initiation of (association) projects.

 

At a glance

Fraunhofer IIS is offering a two-day seminar on machine learning (ML) in a professional environment to help industry in the use of machine learning. In this seminar you will learn to successfully implement ML projects. From the definition of their business goals to the test and their use in live operation. A broad selection of treated algorithms and illustrative examples sharpens the view of the application areas in their company. Another important point is the identification of pitfalls in the processing chain of learning processes, as well as the use of appropriate countermeasures or "best practices".

 

Learning objectives an competences

By participating in the seminar, you will:

• understand the basics of machine learning
• learn to cellect and structure data effeicently
• get to know unsupervised and supervised learning methods
• use proven procedures for quick results and decisions
• get to know examples of the use of machine learning in context
• detect and eliminate errors in the processing chain

 

Content

Seminar: Machine Learning

Day 1: theory and practice	Day 2: Project ans Best practice
Methods of unsupervised learning Practical examples Algorithms of supervised learning Regression Classification Practical examples Insight into process mining for process transparency Practical examples	Implementation of a ML project From business goal to live operation Detect and correct errors Over and underfitting Bias / Variance Trade-Off parameter optimization Unbalanced records Concept drift of data sources

 

Who should attend?

• Technical decision makers
• Developers and engineers in industrial companies
• Inquisitive of all kinds