Machine learning for financial data structures
This course is devoted to applications of machine learning for typical data structures that appear in financial scenarios. Due to the stochastic nature of this data, straightforward fitting machine learning to “predict something in the future” will inevitably fail and lead to the money losses. During four lectures we will review the main financial data structures that we see in practice: time series of prices of some instruments, derivatives and structured portfolios. Concerning time series we will study, how “standard” machine learning techniques may fail on these data types and how to prepare them accordingly. What about options, we will review how supervised learning and reinforcement learning able to speed up and improve the accuracy of the pricing procedure. Last but not least, we will see, how machine learning helps classical portfolio optimization.
Basic understanding of linear algebra and calculus
Basic understanding of probability theory and stochastic processes
Basic understanding of machine learning (logistic/linear regression etc)
Basic-to-intermediate practical skills with Python for data analysis (pandas, NumPy, SciPy)
Basic-to-intermediate practical skills with Python ML frameworks (scikit-learn, Tensorflow, PyTorch)
Experience with neural networks is preferred but not necessarily required
About the lecturer
I am Alex Honchar, machine learning expert with more than 5 years of experience in the field. At the moment I am a partner in Mawi Solutions (working on biosignal analysis) and as an independent consultant with a small team behind I help companies to launch new products based on AI or improve current solutions. Meanwhile, I am also writing in a popular blog on Medium and giving public speeches on conferences and meetups in Ukraine, Italy, and Spain. My mission is to build AI products, that truly outperform human skills or deliver new knowledge that couldn’t be discovered solely with human intellect. Of course, I believe that such products should not leave professionals without their jobs, but the opposite – become valuable partners for achieving more ambitious goals.