• Decision Making under Uncertainty and Reinforcement Learning

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    • Probability and measure theory; Subjective probability; Decision problems; Conjugate priors; Estimation; Hypothesis testing; (Linear models); Sequential sampling; Experiment design; Markov decision proceses; Reinforcement learning; Approximate Dynamic Programming; Bandit problems and Regret; Learning with expert advice; Stochastic optimisation; (Learning in games)
  • Advanced topics in Reinforcement Learning and Decision Making (TBD)
    • Decision problems; Bandit problems; Markov decision proceses; Exact MDP algorithms and computational complexity; Stochastic approximation, asymptotic convergence and PAC learning; Function approximation, neural networks; Bayesian reinforcement learning; Regret bounds; Hierarchical reinforcement learning; Multi-agent RL and human-AI sytems; RL in society
  • Adaptive methods for data-based decision making" - At UiO
  • (1) Reproducibility: Bayesian inference, decision problems; hypothesis testing; (2) Databases, anonymity and privacy; (3) Graphicl models and fairness (4) Recommendation systems and latent variable models (5) Causality, interventions and counterfactuals (6) Experiment design and bandit problems
  • Web Fundamentals - At Lille-3
  • Fundamentals of Data Mining (Algorithmes fondamentaux de fouille de données) - At Lille-3
  • DIT725, Logic, Algorithms and Data Structures. (not this year)
    • Logic, sets, graphs, complexity, search, sorting, dynamic programming, stacks, trees, matrices, graphs.
    • Literature:
      1. Michael T. Goodrich & Roberto Tamassia : Data Structures and Algorithms in Java, 5th Ed John Wiley & Sons ( ISBN: 978-0-470-39880-7 ).
      2. Judith L. Gersting : Mathematical Structures for Computer Science, 6th Ed W H Freeman & Co ( ISBN: 0-7167-6864-X )
  • Discrete Optimization (LP3)

    With Peter Damaschke. This year I am extending the course to include some modern developments in combinatorial bandit optimization

  • Artificial Intelligence (LP4)

    Piazza homepage for homework assignments and feedback

    Course schedule and other details

    With Peter Ljunlof. This year I am modernising the course with the inclusion of probabilistic inference and planning and reinforcement learning; a high-level overview of some of the topics in the decision theory course

  • Individual lectures

    Notes and tutorials

    MSc Projects

    Generally, I am interested in any of the following topics: Below is a list of some specific topics. Feel free to also apply for working on an extension or continuation of a past project.

    Project list

    Past PhD theses