Additional Chapters of Machine Learning

Learning Objectives & Outcomes

Studying the basic algorithms in data analysis, forecasting and machine learning necessary for reading literature and building your own intelligent systems in the financial field.

• Introduction to machine learning.
• Dimension reduction.
• Anomaly detection. Unbalanced classification.
• Clustering.
• Multi-armed bandits and RL.
• Probabilistic approach to machine learning. Bayesian linear regression.
• Nuclear methods. Regression based on Gaussian processes. RKHS space.
• Optimization and active learning based on surrogate models.
• Neural networks. Estimation of parameters of deep neural networks.
• Convolutional neural networks. The use of such models in practice.
• Presentation training. Using self-learning to get insights.
• Recurrent neural networks. The mechanism of attention and transformers.
• Building ensembles of machine and deep learning models.
• Modern generative models. GANs, optimal transport.
• Classical models for working with time series: ARIMA and decomposition of the series into components.
• The use of machine learning in the financial field.