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Probability Theory. Lecture 1

2026LectureСтанислав Шапошников

The finite probability space and five properties of probability, a description of all probability measures on a finite set, equally likely outcomes and the choice of a model, classical paradoxes (two dice, the division of the stakes, Bertrand's paradox), conditional probability and the product rule, the law of total probability as a way to define a measure, Bayes' formula, independence of events.
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170

Geometry. Lecture 2

2026LectureФёдор Попеленский

Finite geometries — the Platonic solids. A reminder — right actions, orbits, stabilisers, fundamental domains (corrected definition) and the example of Z+Z\mathbb{Z} + \mathbb{Z} on the plane; the formula ∣G∣=∣Orb⁡(x)∣⋅∣Stab⁡(x)∣|G| = |\operatorname{Orb}(x)| \cdot |\operatorname{Stab}(x)| and the 48 symmetries of the cube; regular polyhedra — Sosinsky's definition and the definition via flags, a stellated counterexample; the five Platonic solids; regular polytopes in R4\mathbb{R}^4 (six) and in Rn\mathbb{R}^n for n≥5n \ge 5 (the cube, the simplex, the hyperoctahedron) with the coordinates of their vertices; why there are no more than five — face angles and the Schläfli symbol; uniqueness of the gluing and the rigidity theorem; Euler's formula; the finite subgroups of SO(3)SO(3) — Zn\mathbb{Z}_n, DnD_n and the rotation groups of the tetrahedron, cube and icosahedron; the duality cube–octahedron, icosahedron–dodecahedron; which figures on the sphere realise Zn\mathbb{Z}_n, DnD_n and Z2\mathbb{Z}_2.

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380

Vector calculus. Line integrals of the first kind

2026Problem sheetSergey

Integration with respect to arc length: parametrising a curve, the length element ds=∣r′(t)∣ dtds = |\mathbf{r}'(t)|\,dt, arc length, curves in polar coordinates, the mass and centre of mass of a wire, symmetry instead of computation.

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530

Евклидовы пространства

2026LectureTaras Panov

Евклидовы и эрмитовы пространства, скалярное произведение, неравенство Коши–Буняковского и неравенство треугольника, ортогональные системы векторов.
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190

Machine Learning 1. Bayesian Classification Theory

2026LectureКонстантин Вячеславович Воронцов

The probabilistic formulation of classification: prior and posterior class probabilities, class likelihood functions, the maximum a posteriori principle; the optimal Bayes classifier and losses from errors; empirical estimation of prior probabilities and class densities; the naive Bayes classifier, features with exponential-family densities and the linear naive Bayes classifier; a survey of Bayesian classifiers: the Parzen window method, the quadratic discriminant, Fisher's linear discriminant, Gaussian mixtures with diagonal covariance matrices, and the radial basis function network.
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180