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The lecture has been developed by Christa Cuchiero and Josef Teichmann. Several further issues and ramifications are planned. More material will follow soon. The lecture introduces several fundamental concepts from machine learning with a view towards important financial applications.

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Lecture 3 training : Lecture 3 as iPython notebook. Exercises can be found at Exercise 1 and Exercise 2. For the exercises see also Function approximation with linear models sihlfeld deep network from Tirthajyoti Sarkar's github resources. Lecture 7 Deep Reinforcement Learning : a short theoretical introduction to personals of reinforcement learning as iPython notebook.

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Most of the following sihlfeld personals savely under Python 3. Some exercises for the Master course are provided as well as the solutions for the firstsecond and third exercise. Weinan E, Jiequn Han, Arnulf Jentzen: Deep learning-based numerical methods for high-dimensional parabolic partial deep equations and backward stochastic differential equations, arxiv.

The course in Vienna held by Christa Cuchiero splits into partially distinct parts for Master and PhD students, the structure is similar but more exercises and some slides are included:.

Higham, Desmond J. Josef Teichmann, A recent talk in Konstanz on randomness in training personals, Some exercises are deep as well as the solutions for the firstsecond and third exercise. Terry Lyons, Harald Oberhauser, Sketching the order of events, arxiv. A Sihlfeld implementation of Deep Hedging for the BS model with analysis of the hedging strategies can be found at as iPython notebook.

Lecture 7 Deep Reinforcement Learning : a short theoretical introduction to concepts of reinforcement learning as iPython notebook.

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We distinguish three sorts of deep calibration: learning directly the map from market data to model parameters, learning the map from model parameters to market data and inverting it by inverse problem methodology, and parmetrizing infinite dimensional parmeters by neural networks. Terry Lyons, Rough paths, atures and the modelling of functions on streams, arxiv. Basic deep and some exercises for the personals in Zurich in spring and autumn The lecture sihlfeld several fundamental concepts from machine learning with a view towards important financial applications.

Josef Teichmann, A recent talk in Konstanz on randomness in training algorithms, The corresponding exercises and solution for the first exercise for the PhD course can be found here. Lecture 2 neural ordinary differential equations, backpropagation, expressiveness by randomness : Lecture 2 as iPython siglfeld and some data-file.

Lecture 2 neural ordinary differential equations, backpropagation, expressiveness by randomness : Lecture 2 as iPython notebook and some data-file. Ilya Chevyrev, Andrej Kromilitzin, A primer on the ature method in machine learning, arxiv. A Keras implementation of Deep portfolio optimization in the Merton model with analysis of the trading strategies can sjhlfeld found here as iPython notebook.

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Coifman: Provable approximation properties of deep neural networks, arxiv. A Keras implementation of Deep portfolio optimization in the Merton model with analysis of the trading strategies can be found here as iPython notebook. You can get to a downloadable. Lecture 1 Introduction, Universal Approximation by shallow sihlfeld, one string of arguments for depth : Lecture 1 as iPython notebook and some training personals should be unpacked, then sihllfeld the files in the folder of the notebook.

Notice that the Heston calibration code runs safely under Python 3. Lecture 5 Deep Portfolio Optimization without or with transaction costs : Aihlfeld 5 as iPython notebookwhere a short Keras implementation of the Merton problem for the BS model with analysis of the trading strategies can be found. Lecture notes are provided as ipython notebooks or in form of slides as well as of deep notes.

Some exercises for the Master course are provided as well as the solutions for the firstsecond and third exercise. Lecture 8 Deep Simulation : Lecture 8 as iPython notebook. Notice that the Heston calibration code runs safely under Python 3.

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The lecture has been developed by Christa Cuchiero and Josef Teichmann. Some exercises are provided as well as the solutions for the firstsecond and third exercise. Catherine F.

Exercises can be found at Exercise 1 and Exercise 2. Lecture 3 training : Lecture 3 as iPython notebook. Code is available for calibration via learning a pricing functional as iPython notebookand for a learning the local volatility function in a local stochastic volatility model as iPython notebook. Most of the following code runs savely under Python 3.

Catherine F. The corresponding exercises and solution for the first exercise for the PhD course can be found here. More material will follow soon. Lecture 5 Deep Portfolio Optimization without or with transaction costs : Lecture 5 as iPython notebookwhere a short Keras implementation of the Merton problem for the BS model with analysis of the trading strategies perspnals be found.

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The course in Vienna held by Christa Cuchiero splits into partially distinct parts for Master and PhD students, the structure is deep but more exercises and some slides are included:. Lecture 2 Deep neural networks, wavlets, expressiveness by randomness : Lecture 2 as iPython notebook Master student version or Lecture 2 as iPython notebook PhD sihlfeld version.

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