Courses
Decision Making
under Uncertainty and Reinforcement Learning
[Sign up]
- 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:
- Michael T. Goodrich & Roberto Tamassia :
Data Structures and Algorithms in Java, 5th Ed John
Wiley & Sons ( ISBN: 978-0-470-39880-7 ).
- 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
- Bayesian methods for population modelling [PDF
Slides]
- Hierarchical Bayes, Empirical Bayes
- Constrained geometric estimation and hypothesis testing
[PDF]
- Optimization under constraints -- Lagrangian
methods -- Linearisation algorithms -- Hypothesis
testing
- Dynamic programming [PDF]
- Sequential decision making -- Markov
decision processes -- Value functions -- Shortest-path
problems -- Episodic, finite, infinite horizon problems --
Dynamic programming -- Backwards Induction -- Policy
Iteration
Notes and tutorials
- An overview of hypothesis testing [PDF]
A general overview of hypothesis testing is
given. The Bayesian and distribution-free framework to
multiple hypothesis testing and to null hypothesis
testing are discussed. Some practical algorithms are
introduced, together with associated performance
bounds.
- Online statistical estimation for vehicle control: A
tutorial[PDF]
This tutorial examines simple physical
models of vehicle dynamics and overviews methods for
parameter estimation and control. Firstly, techniques for
the estimation of parameters that deal with constraints
are detailed. Secondly, methods for controlling the
system are explained. Thirdly, we discuss trajectory
optimization.