| 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 - We are currently planning to use "flipped classroom" setup. That is, we will upload lecture recordings at the beginning of each week and then hold in-person sessions for answering any questions about the material.
- Lecture/Exercise: Wednesday and Thursday, 14:00-15:30.
- We will primarily communicate through Piazza, while using Moodle to distribute the learning material. The password will be made available on Moodle at a later date.
- Required knowledge: Content of our Machine Learning lecture
- Please join our course on Piazza via the link posted on Moodle. We use Piazza as a platform for answering questions and handling all organizational matteres. Please avoid sending us e-mails!
- If you have any problems signing up for the course on TUMOnline or Moodle, please contact jan.schuchardt [at] in.tum.de
Tentative list of topics - Introduction & Advanced ML Principles
- Machine Learning, Data Mining Process
- Basic Terminology
- Variational Inference
- Deep Generative Models: VAE, Implicit Models, GANs
- Robustness
- Adversarial attacks
- Adversarial training
- Exact robustness verification
- Relaxed robustness certification (Convex relaxation, Lipschitzness, Randomized smoothing)
- 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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