Where is Kumpula Campus?

At the Kumpula Campus of the European Laboratory for Learning and Intelligent Systems Institute, courses focus on theoretical foundations, probabilistic modeling, and scientific applications of AI. The offerings promote research excellence.

• Probabilistic Machine Learning: Bayesian networks, Gaussian processes, MCMC sampling, and uncertainty modeling.
• Advanced Deep Learning: Theory of generalization, adversarial robustness, and self-supervised learning.
• Statistical Learning Theory: VC dimension, PAC learning, empirical risk minimization, and high-dimensional statistics.
• Graphical Models: Directed and undirected models, inference algorithms, and structure learning.
• Causal Inference in AI: Counterfactuals, do-calculus, instrumental variables, and causal discovery.
• Natural Language Understanding: Compositional semantics, reasoning over text, and knowledge graphs.
• Reinforcement Learning Theory: Regret bounds, exploration-exploitation, and bandit algorithms.
• AI for Scientific Discovery: ML in physics, biology, climate modeling, and hypothesis generation.
• Interpretability and Fairness: Causal explanations, counterfactual fairness, and algorithmic auditing.
• Quantum Machine Learning: Quantum circuits, variational quantum algorithms, and hybrid models.

The campus features theoretical seminars and reading groups with ELLIS fellows. Students engage in proof-based projects and publish at top conferences like NeurIPS and ICML. Ties to natural sciences enable cross-disciplinary research in bioinformatics and neuroscience. Doctoral tracks delve into frontier topics like AI safety and trustworthy systems. Graduates pursue academia or research labs worldwide.