August 22-26, 2022, Erasmus University Rotterdam Campus Woudestein, Rotterdam
Gui Liberali is the Full Professor of Digital Marketing at Rotterdam School of Management (RSM) of the Erasmus University. His research interests include optimal learning, multi-armed bandits, sequential decision-making and adaptive sampling in various settings (clinical trials, webdesign, online advertising), dynamic programming, machine learning,
Meet the lecturer
This course studies reinforcement learning methods to model and solve management science and marketing problems that involve an explicit trade-off between learning (exploration) and exploiting the information that has been already acquired (e.g., earning). In particular, we will focus on the class of reinforcement learning problems that can be described and modeled as multi-armed bandits. Applications include online advertising, website optimization, clinical trials, new product development, pricing, revenue management, and consumer search.
The 2022 edition of this course has a strong emphasis on algorithms and an applied nature. This course will give you competence to identify multi-armed bandit problems (MABs), properly model them, and identify appropriate methods to solve them. In addition to gaining competence in MABs in general, you will be exposed to the challenges and methods used to tackle online MABs, i.e., problems that need to be solved in real time.
Reinforcement Learning course focuses on using machine learning methods to model and solve problems relevant to management science problems – in particular, those problems involving machines that autonomously make decisions on the behalf of the modeler, as in online settings.
The course is based mainly on reinforcement learning (when we model states and transitions) and multi-armed bandits (when states are not modelled). We will focus on the design, solution, and implementation of learning methods for sequential decision-making under uncertainty. Sequential decision problems involve a trade-off between exploitation (acting on the information already collected) and exploration (gathering more information). These problems arise in many important domains, ranging from online advertising, clinical trials, website optimization, marketing campaign and revenue management.
We will have lectures in the morning from 9-12 AM CEST and tutorials and computer lab sessions/tutorials in the afternoons from 1-3 PM CEST.
We will use selected chapters from two textbooks on bandits and reinforcement learning (Lattimore and Csaba, 2021 and Sutton and Barto, 2018) and classical papers in management science and marketing science literatures. The detailed syllabus will be available 4 weeks before the start of the course.
This course is targeted at PhD students (and research master students) with a strong background in statistics, econometrics, or computer science.
The course welcomes graduate students enrolled in a broad set of programs, including economics, finance, quantitative marketing, epidemiology, clinical trials, management science, genomics, and others.
Basic knowledge of supervised machine learning (linear regression, logistic regression), unsupervised machine learning methods (PCA, clustering), and coding experience in R (or Python).
|Academic Director||Prof Gui Liberali|
|Credits||Participants who joined at least 80% of all sessions will receive a certificate of participation stating that the summer school is equivalent to a workload of 3 ECTS. Note that it is the student’s own responsibility to get these credits registered at their university.|
|Venue||Erasmus University Rotterdam Campus Woudestein, Burg. Oudlaan 50 Rotterdam.|
|Capacity||30 participants (minimum 15)|
|Fees||Tuition Fees and Payment|
|Application deadline||July 25|
|Apply here||Link to application form|