The aim of this lecture is to introduce some fundamental concepts and techniques from machine learning and deep learning with a view towards important and recent applications in finance : This includes advanced techniques in scoring, hedging/pricing of options, calibration of models, deep portfolio optimization, numerical resolution of high-dimensional non-linear partial differential equations arising for instance in stochastic control and portfolio selection, market generators, deep reinforcement learning and trading portfolio.
- Part I. Fundamental concepts from machine learning
- Presentation of the main machine learning algorithms and specificities to financial time series
- False discovery and back-testing
- Presentation of scoring techniques
- Deep learning: Multi-layer feedforward neural networks, LSTM Backpropagation, stochastic gradient for training Implementation with TensorFlow
- Part II. Applications in finance
- Gaussian process regression and financial applications
- Deep optimization in finance: deep hedging, deep calibration, deep simulation (market generators)
- Neural networks-based algorithms for high-dimensional non linear problems (stochastic control, non linear PDEs)
- Deep reinforcement learning
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C. Huré, H. Pham, X. Warin : Deep backward schemes for high-dimensional nonlinear PDEs, to appear in Mathematics of Computation.
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