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Machine Learning 1. Generalization Theory

2026LectureКонстантин Вячеславович Воронцов

The problem of estimating generalization ability; the error matrix and the probability of overfitting; the case of a single algorithm and the hypergeometric distribution; the principle of uniform convergence of frequencies and Boole's inequality; the Vapnik–Chervonenkis bound, the growth function and the capacity of a family of algorithms; inverting the bound and the complexity penalty; structural risk minimization; the splitting and similarity effects; the Occam's razor bound and its link to regularization.
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140

Machine Learning 2. Lecture 10. Reinforcement Learning

2026LectureКонстантин Вячеславович Воронцов

Reinforcement learning as learning from the environment's response: the multi-armed bandit problem, estimating the value of an action, the greedy and ε-greedy strategies, the exploration–exploitation trade-off, the UCB method, the exponential moving average, the pursuit method and reinforcement comparison; an environment with states, the Markov decision process, how it differs from ordinary machine learning problems, the discounted return, the state and action value functions, the Bellman equation, the SARSA and Q-learning methods, on-policy and off-policy; deep Q-learning DQN on the example of Atari games, experience replay; gradient optimisation of the policy (policy gradient) and its connection with log-likelihood maximisation; model-based and model-free approaches, a regression model of the reward with an upper confidence bound, LinUCB, recommending news articles, evaluating a policy on historical data.
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550

Topology 2. Lecture 2

2026LectureФёдор Вылегжанин

Where simplicial homology shows up in real life — topological data analysis. The union of balls around a point cloud, persistence modules and their decomposition into a sum of interval modules over a field (the structure theorem and the algorithm), the barcode; the Vietoris–Rips and Čech complexes; the stability theorem for barcodes, the Hausdorff, Gromov–Hausdorff and Wasserstein distances; open covers and their nerves, Leray covers and the nerve theorem, the category of covers, paracompactness, Lebesgue covering dimension, Čech homology and cohomology.
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520

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

Machine Learning 1. Lecture 6. Linear Regression Models

2026LectureКонстантин Вячеславович Воронцов

Multiple linear regression and least squares, the pseudoinverse and the geometric meaning of least squares; the solution through the singular value decomposition; multicollinearity and the condition number; ridge regression, choosing the regularization parameter and the effective dimension; LASSO and non-smooth regularizers for feature selection; principal component analysis as a low-rank matrix decomposition; the spectral method of least squares.
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130

Machine Learning 1. Lecture 8. Feature Selection, Model Evaluation and Model Selection

2026LectureКонстантин Вячеславович Воронцов

Measures of classification quality: type I and type II errors, the ROC curve and AUC, gradient maximization of AUC, log-loss, precision and recall, micro- and macro-averaging, the F-measure; internal and external criteria of generalization ability; hold-out, leave-one-out, q-fold and t×q-fold cross-validation, stratification; model consistency; regularization, AIC, BIC and the Vapnik–Chervonenkis bound; multi-criteria model selection; feature selection: full search, Add, Add-Del, depth-first search, beam search (GMDH), the genetic algorithm and random search with adaptation.
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150

Machine Learning 1. Lecture 11. Advanced Ensemble Methods

2026LectureКонстантин Вячеславович Воронцов

Bias–variance analysis: the decomposition of the error into noise, bias and variance, the bias and variance of simple voting, why complex ensembles do not overfit; gradient boosting for an arbitrary loss function as a parametric approximation of a gradient step; stochastic gradient boosting; special cases: regression, AdaBoost, AnyBoost, variants of loss functions; XGBoost: the tree as a linear model, L0 and L2 regularisation, analytical leaf values and the splitting criterion; CatBoost: leakage in the gradients, ordered boosting on subsamples of doubling length, unbiased target statistics for categorical features.
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190

Machine Learning 2. Adaptive Forecasting Methods

2026LectureКонстантин Вячеславович Воронцов

Time series forecasting problems: the time series model, the forecasting horizon, the least squares method; econometrics as the main source of problems — trends, seasonalities, change points; examples of forecasting sales volumes and electricity prices; the linear autoregression model; a brief survey of forecasting methods; adaptive short-term forecasting methods: the exponential moving average and the smoothing parameter, the models of Holt, Theil–Wage, Winters with linear and exponential trend, adaptive autoregression; the tracking signal and a statistical test of model adequacy, the Trigg–Leach model; adaptive selection and adaptive composition of models, the LAVR method — local adaptation of weights with regularisation, the monotonicity constraint as a regulariser; experiments on the sales forecasting problem.
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140

Mathematical Analysis 1. Lecture 1

2026LectureСтанислав Шапошников

Naive set theory and Russell's paradox, Kuratowski's ordered pair and the Cartesian product, a function as a subset of a product, composition, injections, surjections and bijections, the group of bijections and the embedding of any group into it, equinumerosity and indicator functions, the Cantor–Bernstein and Cantor theorems.
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290

Algebra 1. Lecture 1

2026LectureАртём Авилов

What algebra studies — algebraic structures. Groups (definition, examples, additive notation, uniqueness of the identity and of inverses), rings (variants of the axioms, rings with and without identity), fields and division rings, vector spaces and modules, algebras over a field. The symmetric group SnS_n — permutations, non-commutativity, two-row notation, cycles and transpositions, decomposition into transpositions, disjoint permutations commute, decomposition into disjoint cycles and its uniqueness, powers, inversions, parity and the sign of a permutation, multiplying by a transposition changes the sign.

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310

Algebra 1. Lecture 2

2026LectureАртём Авилов

The order of an element and of a group, the order of a permutation as the lcm of its cycle lengths; generators and cyclic groups; homomorphisms and isomorphisms of groups, the classification of cyclic groups; examples — the sign of a permutation, inversion in an abelian group, the exponential, the determinant, S3S_3 and D3D_3; subgroups, kernel and image, the injectivity criterion; left and right cosets and the partition into them; normal subgroups and conjugation, the index, Lagrange's theorem, subgroups of index 2; the quotient group; the homomorphism theorem with examples; conjugate elements, the centre, a group action on a set.

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150

Geometry. Lecture 1

2026LectureФёдор Попеленский

What geometries there are and what they have in common — Klein's Erlangen programme; the school 3–4–5 triangle and the ellipsoid; the cube and the brick as finite geometries; metric spaces; groups, quantifiers in the axioms; the group of bijections and the order of composition; left and right actions of a group on a set; the metrics d1d_1, d2d_2, d∞d_\infty on Rn\mathbb{R}^n; isometries, subgroups; the order of an element; orbits, stabilisers, transitive actions; the list of geometries in the course — the Euclidean plane, the regular triangle and S3S_3, the cube and its 48 symmetries, the Poincaré model of the Lobachevsky plane; fundamental domains; subgeometries — the square in the cube.

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330
Фёдор Вылегжанин
@fedor-vylegzhanin

Фёдор Евгеньевич Вылегжанин — стажёр-исследователь отдела геометрии и топологии Математического института им. В. А. Стеклова РАН (с 2023 года) и аспирант МИАН по специальности «Геометрия и топология» (с 2024 года); стажёр-исследователь Лаборатории топологии и комплексных сетей факультета математики НИУ ВШЭ. Окончил механико-математический факультет МГУ имени М. В. Ломоносова (2024) по специальности «Фундаментальная математика и механика». В 2022–2025 годах работал в Международной лаборатории алгебраической топологии и её приложений НИУ ВШЭ. В НИУ ВШЭ вёл занятия по курсу «Математический анализ 2» (2025/26); в Независимом московском университете осенью 2026 года читает курс «Топология-2».