Applied Machine Intelligence
This course teaches concepts of information extraction using machine learning in various applications taking into account constraints in realistic use-cases. Typical topics are:
- Lifecycle of a Machine Learning task
- Data Preprocessing
- Regression (Algorithms and Metrics)
- Classification (Algorithms and Metrics)
- Deep Learning
- Active Learning
- Model Selection
- Validation Techniques
- Model Interpretation
Task Description
In this working student position you work on the following topics:
- Setup and maintain a Kubernetes Cluster on an LRZ Server machine
- Provide access for participants of the Applied Machine Intelligence lecture
- Support students to deploy their projects on the cluster
- Integrate Data Science Storage by LRZ
- Provide access to virtual Nvidia GPUs for each project group on the server machine
Requirements
- Intermediate knowledge of Kubernetes and Docker
- Intermediate knowledge of version control systems (Git)
- Interest in system administration and IT technology
- High motivation and independent way of working, proactive and self-organized commitment
- Team player skills
Our offerings
- Integration in a team of data researchers at the Chair of Data Processing
- Work can be done completely from a remote position if required
- The payment is in the usual range for student assistants
- Start from approx. beginning of April until approx. middle of July
- The duration of work is approximately 6 hours per week, but this can be flexibly adjusted.
- Access to student rooms and equipment
- Unlimited supply of coffee (personal use only)
Contact: ami.ldv(at)xcit.tum.de
