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 inperson sessions for answering any questions about the material.
 Lecture/Exercise: Wednesday and Thursday, 14:0015:30.
 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 emails!
 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
