# Математические методы прогнозирования (лекции, В.В. Стрижов)/Группа 774, осень 2020

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 Версия 08:36, 20 октября 2020 (править)← К предыдущему изменению Версия 08:37, 20 октября 2020 (править) (отменить)К следующему изменению → Строка 1: Строка 1: - Лекции по четвергам в 10:30 тут: [http://m1p.org/go_zoom m1p.org/go_zoom] + '''Лекции по четвергам в 10:30 тут: [http://m1p.org/go_zoom m1p.org/go_zoom]''' + {{Main|Численные методы обучения по прецедентам (практика, В.В. Стрижов)}} + __NOTOC__ This course joins two parts of the problem statements in Machine Learning. The first part comes from the structure of the measured data. The data come from Physics, Chemistry and Biology and have intrinsic algebraic structure. This stricture is part of the theory that stands behind the measurement. The second part comes from errors of the measurement. The stochastic nature errors request the statistical methods of analysis. So this course joins algebra and statistics. It is devoted to the problem of predictive model selection. This course joins two parts of the problem statements in Machine Learning. The first part comes from the structure of the measured data. The data come from Physics, Chemistry and Biology and have intrinsic algebraic structure. This stricture is part of the theory that stands behind the measurement. The second part comes from errors of the measurement. The stochastic nature errors request the statistical methods of analysis. So this course joins algebra and statistics. It is devoted to the problem of predictive model selection.

## Версия 08:37, 20 октября 2020

Лекции по четвергам в 10:30 тут: m1p.org/go_zoom

This course joins two parts of the problem statements in Machine Learning. The first part comes from the structure of the measured data. The data come from Physics, Chemistry and Biology and have intrinsic algebraic structure. This stricture is part of the theory that stands behind the measurement. The second part comes from errors of the measurement. The stochastic nature errors request the statistical methods of analysis. So this course joins algebra and statistics. It is devoted to the problem of predictive model selection.

The course holds two semesters: Fall 2020 and Spring 2021. It contains lectures and practical works. Out of schedule cuts off half the score. The scoring, max:

1. Questionnaires during lectures (3)
2. Two application projects (2+2)
3. The final exam: problems with discussion (3)

# Schedule

 Date N Subject Link September 3 1 Probabilistic models Slides 10 2 Models: regression, encoders, and neural networks 17 3 Processes: bayesian regression, generative and discriminative models 24 4 Functional data analysis: decomposition of processes 31 5 Spatiotemporal models October 8 6 Convolutional models 15 7 Talks for the fist part of lab-projects The talk template 22 8 Graph convolutions and spectrum 29 9 Fourier transform and phase retrieval problem 4 10 Radon transform and tomography reconstruction November 12 11 Tensor decomposition and decoding problem 19 12 Statistics on riemannian spaces 26 13 Statistics on stratified spaces December 3 14 Talks for the second part of lab-projects The talk template 4 15 Exam: problems and discussion List of problems

# Topics

• Forward and inverse problems, kernel regularisation
• Karhunen–Loeve theorem, FPCA
• Parametric and non-parametric models
• Reproductive kernel Hilbert space
• Integral operators and Mercer theorem Convolution theorem
• Graph convolution
• Manifolds and local models

L3 courses towards machine learning

• Functional analysis
• Differential geometry

# References

1. Functional data analysis by James Ramsay, Bernard Silverman, 2020
2. Riemannian geometric statistics in medical image analysis. Edited by Xavier Pennec, Stefan Sommer, and Tom Fletcher, 2020
3. Manifolds, tensors and forms by Paul Renteln, 2014
4. Theoretical Foundations of Functional Data Analysis, with an Introduction to Linear Operators by Tailen Hsiing, Randall Eubank, 2013
5. At the Interface of Algebra and Statistics by Tai-Danae Bradley, 2020