After successful completion of the course, students are able to search the literature on their own, to write seminar papers using the LaTeX typesetting system, and to prepare and give presentations (45 minutes).

• Recent topics in supervised learning
• Recent topics in unsupervised learning
• Generative adversarial networks (GAN)
• Reinforcement learning: various topics
• Reinforcement learning: Atari, Go, etc.
• Artificial neural networks, deep learning
• Bayesian estimation (for PDE): numerical algorithms
• Bayesian estimation (for PDE): theory
• Image classification
• Autonomous driving
• Etc.

Supervised machine learning, unsupervised machine learning, reinforcement learning.

Semianr paper and seminar presentation.

Linear algebra, analysis.