Machine Learning Lab

Practical course: Large-Scale Machine Learning (IN2106, IN4192)

You are invited to join the final presentations of this lab course projects. The groups will begin with short overview presentations and then be available at their posters for more in-depth explanations of their projects and to answer your questions. Join us on Thursday, February 9th, at 10 am in room 00.13.054 to learn about

• uncertainty estimation for autonomous driving,
• anomaly detection in financial data,
• time series forecasting for load prediction,
• and link building for page-rank optimization

in cooperation with Continental, Siemens, energy4u and Ippen Digital!

Application

The pre-course meeting with information regarding the course format, possible topics etc. is scheduled for Jul 18, 2022 2pm on zoom (Passcode: 021039): slides.

Note that you have to register via the matching system and fill out our application form to apply for a spot!

Overview

Machine learning has become one of the most popular fields in research and industry in recent years. Therefore, hands-on machine learning skills are much sought after. In this practical course, students will work in small groups to solve problems on real-world data with state-of-the-art algorithms. Students will be provided with cutting-edge GPU resources to facilitate their work on large-scale datasets. We plan to cooperate with industry partners for data and projects.

The objective of this lab course (Master-Praktikum) is to develop data mining/machine learning algorithms specifically handling large real-world datasets. Besides focusing on existing principles, the participants will also design and realize novel analysis techniques.

All organizational aspects will be addressed at the organizational meeting. In the application form, interested students should enter relevant information about themselves, which will be used for the admission process. More information bellow. 

Information

• Prerequisites:
  • The lab course is designed for Master students of Computer Science (including Data Engineering & Analytics program etc.)
  • Good knowledge in data mining/machine learning is a must (i.e. at least one of the related lectures "Machine Learning", "Machine Learning for Graphs and Sequential Data" etc.).
  • Since the lab course focuses on the implementation of data mining/machine learning algorithms, strong programming skills (preferably in Python) are required. In particular, knowledge in frameworks such as Tensorflow and PyTorch will be helpful.