Lecture: Machine Learning for Graphs and Sequential Data

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

• As long as the coronavirus situation does not allow for in-person lectures, we will upload videos of lectures and tutorials, and provide pointers to other reference materials.
• 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. The password will be made available on Moodle at a later date.
• Required knowledge: Content of our Machine Learning lecture

Tentative list of topics

1. Introduction & Advanced ML Principles
   • Machine Learning, Data Mining Process
   • Basic Terminology
   • Variational Inference
   • Deep Generative Models: VAE, Implicit Models, GANs
2. Sequential Data
   • ML models for text data and temporal data
   • Autoregressive Models
   • HMMs, Kalman Filter
   • Embeddings (e.g. Word2Vec)
   • Neural Networks (e.g. RNN, LSTM)
   • Temporal Point Processes
3. Graphs & Networks
   • Laws, Patterns
   • (Deep) Generative Models for Graphs
   • Spectral Methods
   • Ranking (e.g., PageRank, HITS)
   • Community Detection
   • Node/Graph Classification
   • Label Propagation
   • Graph Neural Networks
   • (Unsupervised) Node Embeddings
   • Dynamic/temporal graphs