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This project course introduces students to data-driven engineering of networks and cloud systems. Using methods from statistical learning, students will develop and evaluate, for instance, models for prediction and forecasting of Key Performance Indicators (KPIs) and for anomaly detection. The models will be fitted and evaluated using testbed measurements or traces from operational systems. The functions built from these models are designed for real-time execution.
To develop the models, tools and packages from data science will be used, e.g., Jupyter notebook, scikit-learn, TensorFlow.
The course is structured as two consecutive project blocks. Each block starts with introductory lectures that give background and discuss concepts for the specific project, followed by project execution, writing of a short report, and interview.
Requirements for passing the course are that the student successfully completes both course projects and passes an assessment interview.
Course goals
After passing this course, participants should be able to:
- perform the modeling of a network analytics task
- pre-process data and create predictive models using machine-learning techniques and tools
- assess, interpret and possibly apply the results
- produce a written report describing and explaining the project results
Prerequisites:
Basic knowledge in statistics, machine learning, networking, and computing systems. The projects require programming in Python.
Search terms:
Machine learning, statistical learning, project course, networking, cloud computing, programming
Literature:
Course documentation and background literature will be available through the course web site.