Optimization problems need to be solved in almost every domain. Nurses and doctors, for example, need to be assigned to shifts in a hospital without violating given constraints, investment portfolios have to be built in order to maximize the return, or the many parameters of a weather forecast model have to be chosen in order to best fit previously measured data. Machine learning is no exception: optimization problems occur, for example, in reinforcement learning or during the hyperparameter tuning of machine learning algorithms.
This introductory course on optimization for machine learning aims at teaching the basic knowledge about optimization theory and the design and analysis of optimization algorithms. One goal is to provide the necessary background that will allow the participants to practically address the various optimization problems they might encounter in the future.
The lecture covers both fundamental aspects in discrete optimization (such as greedy algorithms, dynamic programming, heuristics) and continuous optimization (introducing gradient-based methods as well as derivative-free algorithms).
The course will be taught on Thursday afternoons from 2pm till 5pm.
If you wish, please send an email with your mobile phone number to Dimo Brockhoff with title "Register for TC2 Optimization class" to register for our whatsapp group.
The lecture is given by Anne Auger and Dimo Brockhoff from November till December 2021.
| Anne Auger and Dimo Brockhoff | ||||||
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