Difference between revisions of "Mathematical forecasting"
From Research management course
(Created page with "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...") |
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# The final exam | # The final exam | ||
| − | + | =Topics= | |
| − | Karhunen–Lo`eve theorem, FPCA | + | * Forward and inverse problems, kernel regularisation |
| − | Parametric and non-parametric models | + | * Karhunen–Lo`eve theorem, FPCA |
| − | Reproductive kernel Hilbert space Integral operators and Mercer theorem Convolution theorem | + | * Parametric and non-parametric models |
| − | Graph convolution | + | * Reproductive kernel Hilbert space |
| − | Manifolds and local models | + | * Integral operators and Mercer theorem Convolution theorem |
| + | * Graph convolution | ||
| + | * Manifolds and local models | ||
| + | |||
L3 courses towards machine learning | L3 courses towards machine learning | ||
| − | Functional analysis Differential geometry | + | * Functional analysis |
| − | + | * Differential geometry | |
| + | |||
| + | =References= | ||
| + | # FDA by T. Hsiing, R. Eubank | ||
| + | # FDA by J. Ramsay, B. Silverman | ||
Revision as of 00:57, 6 August 2020
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. The scoring:
- Questionnaires during lectures
- Two application projects
- The final exam
Topics
- Forward and inverse problems, kernel regularisation
- Karhunen–Lo`eve 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
- FDA by T. Hsiing, R. Eubank
- FDA by J. Ramsay, B. Silverman