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Additional Chapters of Machine Learning

Recommended for the 4-6th year of Specialist’s program, 4th year of Bachelor’s program, and 1-2nd year of Master’s program

Start: February 8, 2022

Lectures
Day: Tuesday
Time: 6:30-8:05pm
Language: Russian/English
Format: online

Seminars
Day:  Tuesday
Time: 8:10-9:40pm
Language: Russian
Format: online

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.

Program of the course

  • 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.
See full course outline.