This workshop offers detailed insights into the latest Statistical Machine Learning techniques applied in Quantitative Finance. Participants will explore topics such as derivatives pricing, calibration, hedging, and time series management. The course includes sophisticated modeling approaches for both Q-quant and P-quant strategies, providing a comprehensive theoretical introduction complemented by concrete examples. Live demonstrations of computational methods will enhance the learning experience.

Additionally, the workshop covers the setup of methods in Python using various libraries, including NumPy, SciKit Learn, and TensorFlow/PyTorch. The selected examples are directly linked to practical applications in Quantitative Finance, with all materials available for further exploration post-course, including Python code and Jupyter notebooks.

This workshop aims to illustrate the application of cutting-edge Statistical Learning techniques in Quantitative Finance, elevating participants’ skills. However, it should be noted that the theory of Natural Language Processing and GPT will not be included in this course.