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    • Stephan Günnemann
    • Sirine Ayadi
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    • Dominik Fuchsgruber
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  • Teaching
    • Sommersemester 2025
      • Advanced Machine Learning: Deep Generative Models
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Current Topics in Machine Learning
    • Wintersemester 2024/25
      • Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Current Topics in Machine Learning
    • Sommersemester 2024
      • Machine Learning for Graphs and Sequential Data
      • Advanced Machine Learning: Deep Generative Models
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
    • Wintersemester 2023/24
      • Machine Learning
      • Applied Machine Learning
      • Seminar: Selected Topics in Machine Learning Research
      • Seminar: Machine Learning for Sequential Decision Making
    • Sommersemester 2023
      • Machine Learning for Graphs and Sequential Data
      • Advanced Machine Learning: Deep Generative Models
      • Large-Scale Machine Learning
      • Seminar
    • Wintersemester 2022/23
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2022
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar (Selected Topics)
      • Seminar (Time Series)
    • Winter Term 2021/22
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2021
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Winter Term 2020/21
      • Machine Learning
      • Large-Scale Machine Learning
      • Seminar
    • Summer Term 2020
      • Machine Learning for Graphs and Sequential Data
      • Large-Scale Machine Learning
      • Seminar
    • Winter Term 2019/2020
      • Machine Learning
      • Large-Scale Machine Learning
    • Summer Term 2019
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2018/2019
      • Machine Learning
      • Large-Scale Machine Learning
      • Oberseminar
    • Summer Term 2018
      • Mining Massive Datasets
      • Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2017/2018
      • Machine Learning
      • Oberseminar
    • Summer Term 2017
      • Robust Data Mining Techniques
      • Efficient Inference and Large-Scale Machine Learning
      • Oberseminar
    • Winter Term 2016/2017
      • Mining Massive Datasets
    • Sommersemester 2016
      • Large-Scale Graph Analytics and Machine Learning
    • Wintersemester 2015/16
      • Mining Massive Datasets
    • Sommersemester 2015
      • Data Science in the Era of Big Data
    • Machine Learning Lab
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  4. Large-Scale Machine Learning

Machine Learning Lab

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

Application

Watch our information event with presentations from current participants online on Wed, 15.07.2020 at 12:00. Slides pdf 

IMPORTANT! To apply fill out a google form AND register via the matching system!

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

  • Preliminary meeting with final presentations from current semester: Wed, 15.07.2020, 12:00 online. All students who are interested in the lab course are invited to attend.
  • 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 "Mining Massive Datasets", "Machine Learning" etc.).
    • Since the lab course focuses on the implementation of data mining/machine learning algorithms, strong programming skills (in Python, C++, R, or Java) are required. In particular, knowledge in frameworks such as Tensorflow and PyTorch will be helpful.
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Informatics 26 - Data Analytics and Machine Learning


Prof. Dr. Stephan Günnemann

Technical University of Munich
TUM School of Computation, Information and Technology
Department of Computer Science 
Boltzmannstr. 3
85748 Garching
Germany

Secretary's office:
Room 00.11.057
Phone: +49 89 289-17256
Fax: +49 89 289-17257

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