Skip to content
  • de
  • en
  • Data Analytics and Machine Learning Group
  • TUM School of Computation, Information and Technology
  • Technical University of Munich
Technical University of Munich
  • Home
  • Team
    • Stephan Günnemann
    • Sirine Ayadi
    • Tim Beyer
    • Jonas Dornbusch
    • Eike Eberhard
    • Dominik Fuchsgruber
    • Nicholas Gao
    • Simon Geisler
    • Lukas Gosch
    • Filippo Guerranti
    • Leon Hetzel
    • Niklas Kemper
    • Amine Ketata
    • Marcel Kollovieh
    • Anna-Kathrin Kopetzki
    • Arthur Kosmala
    • Aleksei Kuvshinov
    • Richard Leibrandt
    • Marten Lienen
    • David Lüdke
    • Aman Saxena
    • Sebastian Schmidt
    • Yan Scholten
    • Jan Schuchardt
    • Leo Schwinn
    • Johanna Sommer
    • Tom Wollschläger
    • Alumni
      • Amir Akbarnejad
      • Roberto Alonso
      • Bertrand Charpentier
      • Marin Bilos
      • Aleksandar Bojchevski
      • Johannes Klicpera
      • Maria Kaiser
      • Richard Kurle
      • Hao Lin
      • John Rachwan
      • Oleksandr Shchur
      • Armin Moin
      • Daniel Zügner
  • 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
  • Research
    • Robust Machine Learning
    • Machine Learning for Graphs/Networks
    • Machine Learning for Temporal and Dynamical Data
    • Bayesian (Deep) Learning / Uncertainty
    • Efficient ML
    • Code
  • Publications
  • Open Positions
    • FAQ
  • Open Theses
  1. Home
  2. Teaching
  3. Summer Term 2019
  4. Mining Massive Datasets

Lecture: Mining Massive Datasets

This course builds upon the knowledge you gained in the lecture Machine Learning (IN2064). It provides advanced learning principles and covers more complex data domains. Put simply: This course is "Machine Learning 2".

Information: The number of course participants is limited this year (to ensure a high quality correction of the project tasks and taking into account the limited personal capacity available). The selection of participants will be done after the closing date of the registration period. That is, we will not follow a "first come, first serve" principle.

Overview

In this course, you will learn advanced machine learning and data mining techniques to process complex and large-scale data. We will specifically focus on the learning techniques for (i) graphs/network data and (ii) temporal data/sequences. Since in many of today's applications the considered data is further very large, we also discuss how scalable mining and learning can be achieved. The practical relevance of these methods will be highlighted by multiple important applications such as time series segmentation, ranking, or community detection.

The preliminary syllabus of the course is as follow

  • Introduction
    • Machine Learning, Data Mining Process
    • Basic Terminology
  • Scalability
    • Similarity Estimation
    • Filter-Refine Paradigm
    • Hashing & Sketches
      • Min-Hashing
      • Locality Sensitive Hashing
    • Membership Test / Bloom Filter
    • Large-Scale Optimization
  • Temporal Data & Sequences
    • Autoregressive Models
    • HMMs
    • Embeddings (e.g. Word2Vec)
    • Neural Networks (e.g. RNN, LSTM)
  • Graphs & Networks
    • Laws, Patterns
    • (Deep) Generative Models
      • VAE, Implicit Models
      • Generative Models for Graphs
    • Spectral Methods
      • Ranking (e.g., PageRank, HITS)
      • Community Detection
    • Representation Learning for Graphs
      • Graph Neural Networks
      • (Unsupervised) Node Embeddings

Information

  • Lecture/Exercise: Wednesdays, 2:15pm, Interims Hörsaal 1
  • Lecture/Exercise: Thursdays, 2:15pm, Interims Hörsaal 1
  • All course material will be made available via Piazza
  • Required knowledge: Content of our Machine Learning lecture

Literature

  • Mining of Massive Datasets. Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman. Cambridge University Press. 2014
  • Pattern Recognition and Machine Learning. Christopher Bishop. Springer-Verlag New York. 2006.
  • Machine Learning: A Probabilistic Perspective. Kevin Murphy. MIT Press. 2012
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, Jerome Friedman. Springer. 2013
To top

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

  • Privacy
  • Imprint
  • Accessibility