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- Introduction & Advanced ML Principles
- Machine Learning, Data Mining Process
- Basic Terminology
- Variational Inference
- Deep Generative Models: VAE, Implicit Models, GANs
- 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
- 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
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