LibreTimes

Search results for “обучение ранжированию”

36 results

Machine Learning 1. Lecture 13. Clustering and Semi-Supervised Learning

2026LectureКонстантин Вячеславович Воронцов

The clustering problem and its ill-posedness, the aims of clustering, types of cluster structure, sensitivity to the choice of metric; the problem of semi-supervised learning and transductive learning, why it reduces neither to classification nor to clustering; criteria of clustering quality, the silhouette coefficient, the BCubed measures of precision and recall; the k-means method (Lloyd's algorithm) as a simplification of the EM algorithm for a mixture of Gaussians; the DBSCAN algorithm; agglomerative hierarchical clustering, the Lance–Williams formula and its special cases, the dendrogram, monotonicity, contracting and dilating distances, Ward's distance; semi-supervised learning based on classification: self-training, co-training, co-learning, the transductive SVM, expectation regularisation (XR).
0
280

Machine Learning 1. Lecture 14. Anomaly Detection and Robust Methods

2026LectureКонстантин Вячеславович Воронцов

Anomaly and novelty detection; heuristic anomaly scores: local density, isolation forest, a mixture with a background component, robust principal component analysis; outlier removal in nonparametric regression, the LOWESS algorithm; anomaly as the value of the loss function; the theory of robust learning: slowly growing functions, iterative reweighting of objects with IRS and IRLS, robust loss functions, the median, the quantile and the censored mean, robust aggregation functions and their smoothing, the IR-ERM algorithm; one-class SVM, learning from positive and unlabeled objects (PU-learning), problems with new and unknown classes.
0
480

Machine Learning 2. Lecture 1. Deep Neural Networks

2026LectureКонстантин Вячеславович Воронцов

Reminder: the neuron model, the multilayer network, stochastic gradient, momentum, vanishing and exploding gradients, ReLU, dropout, batch normalisation; depth matters more than width; feature generation and end-to-end learning; convolutional neural networks: convolution and pooling layers, the standard CNN scheme, AlexNet and ImageNet, residual networks ResNet and Highway Networks; convolutional networks for speech, texts and games; recurrent neural networks, training by BPTT, long short-term memory networks LSTM, the peephole variant, GRU.
1
390

Machine Learning 2. Lecture 2. Neural Networks for Unsupervised Learning

2026LectureКонстантин Вячеславович Воронцов

Kohonen networks: clustering as a two-layer neural network, the hard (WTA) and soft (WTM) competition rules, self-organising Kohonen maps and their interpretation; autoencoders: the formulation of the problem, the linear autoencoder and the principal component method, sparse, denoising, contractive, relational and variational autoencoders, reparametrisation, autoencoders for supervised learning, multilayer autoencoders; joint training of several models: pre-training and transfer learning, multi-task learning and few-shot learning, self-supervised learning, distillation and surrogate modelling, learning using privileged information (LUPI), generative adversarial networks (GAN).
0
210

Machine Learning 2. Lecture 3. Vector Representations of Texts and Graphs

2026LectureКонстантин Вячеславович Воронцов

Vector representations (embeddings) of texts and graphs: the distributional semantics hypothesis, syntagmatic and paradigmatic similarity of words, the CBOW and Skip-gram models of the word2vec program, hierarchical SoftMax on a Huffman tree, negative sampling (SGNS) and the connection of word2vec with the matrix factorisation of the PMI matrix, semantic analogies of words, the FastText model on character n-grams; texts as graphs; multidimensional scaling and the stress criterion, stochastic neighbour embedding SNE, perplexity, t-SNE; matrix factorisations of graphs, the random-walk models DeepWalk and node2vec; graph embedding methods as autoencoders, the generalised GraphEDM model, graph neural networks and message passing.
0
250

Machine Learning 2. Lecture 4. Attention Models and Transformers

2026LectureКонстантин Вячеславович Воронцов

Sequence-to-sequence problems: the recurrent network, the encoder–decoder architecture and its limitations; the recurrent network with an attention model, interpreting the attention matrix in machine translation and image captioning; similarity functions, the query–key–value attention model, the attention formula, self-attention; multi-head attention and its regularisation, hierarchical attention for document classification, the graph attention model GAT; the transformer for machine translation: the encoder, positional encoding, layer normalisation and skip connections, the decoder with masking, the training criterion, BLEU and WER; the transformer encoder BERT, the MLM and NSP criteria, fine-tuning, BPE and WordPiece tokenisation, model sizes.
1
450

Machine Learning 2. Lecture 5. Topic Modelling

2026LectureКонстантин Вячеславович Воронцов

Probabilistic topic modelling of text document collections: the bag-of-words and conditional independence hypotheses, the maximum likelihood criterion, three interpretations of the problem (soft clustering, stochastic matrix factorisation, autoencoder); the lemma on maximising a function on unit simplices; the ill-posedness of the problem and the additive regularisation of topic models ARTM, the EM algorithm and its interpretation; PLSA and LDA, the Dirichlet distribution, the maximum a posteriori probability and giving up Bayesian inference; smoothing, sparsing, background topics, decorrelation; multimodal and multilingual models, unigrams and bigrams; regularisers for classification and regression on texts, for word co-occurrence in short texts, for links between documents and for topic hierarchies.
0
130

Machine Learning 2. Lecture 8. Association Rule Mining

2026LectureКонстантин Вячеславович Воронцов

Association rule mining as unsupervised learning: binary features, support and frequent itemsets, the association rule, minimum support MinSupp and minimum confidence MinConf; market basket analysis; the link between association rules and logical regularities; the anti-monotonicity of support; the APriori algorithm: breadth-first search for frequent itemsets and recursive extraction of rules; modifications: search on a random subsample, item hierarchies, incremental and decremental algorithms; the FP-growth algorithm: the prefix FP-tree, building it in one pass over the transaction database, computing the support of any itemset from the tree, the conditional FP-tree and recursive search for frequent itemsets; the speed of FP-growth compared with APriori.
0
160

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.
0
140

Machine Learning 2. Lecture 11. Active Learning

2026LectureКонстантин Вячеславович Воронцов

Active learning: the model itself chooses which objects to give the teacher for labelling. The problem setting, why active learning is faster than passive learning, learning curves; pool-based sampling, query synthesis and stream-based selective sampling, cost-sensitive methods; uncertainty sampling (least confidence, minimum margin, maximum entropy), query by committee, version space reduction, expected model change, expected error reduction; query synthesis by the Nelder–Mead method and by the variance reduction criterion; density weighting; exploratory actions, the ε-active and EG-active algorithms with the exponentiated gradient; active learning in crowdsourcing: reconciling annotators' labels, models of their reliability and of object difficulty, assigning tasks to annotators.
0
450

Machine Learning 2. Lecture 12. Interpretability and Explainability

2026LectureКонстантин Вячеславович Воронцов

Interpretability and explainability of machine learning models: goals, tasks and basic notions, who needs explanations and why; interpretable models; feature importance in linear regression: the t-statistic, net effects, contributions and situational importance; partial dependence plots (PDP) and individual conditional expectation plots (ICE); permutation feature importance; the Shapley value from cooperative game theory, its axioms and Monte Carlo estimation, its use in linear regression; surrogate modelling in the neighbourhood of an object, the LIME, Anchors, SHAP, Shapley Kernel and SAGE methods; the Shapley value for training objects, Data Shapley and Gradient Shapley; counterfactual explanations.
0
230

Machine Learning. Additional Lecture 1. The Theory of Overfitting

2026LectureКонстантин Вячеславович Воронцов

The combinatorial theory of overfitting: the probability of overfitting and why the Vapnik–Chervonenkis bounds are loose; experiments with overfitting — a monotone chain of algorithms, the effect of splitting and connectivity of the family on the probability of overfitting and on the complete cross-validation estimate, overfitting of chains, the exact formula for the probability of overfitting when choosing between two algorithms; the splitting–connectivity graph of a family of algorithms, examples for linear classifiers, a monotone chain and a two-dimensional net; generating and forbidden sets, upper connectivity and inferiority of an algorithm; the splitting–connectivity upper bound, the idea of its proof and its properties.
1
220

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.
0
290
Константин Вячеславович Воронцов
@konstantin_vorontsov

Константин Вячеславович Воронцов — профессор РАН, доктор физико-математических наук. Заведующий кафедрой «Математические методы прогнозирования» ВМК МГУ, где с 2021 года — профессор; заведующий лабораторией «Машинное обучение и семантический анализ» Института искусственного интеллекта МГУ (с 2022 года); заведующий кафедрой «Машинное обучение и цифровая гуманитаристика» МФТИ (с 2021 года); заведующий кафедрой «Интеллектуальные системы» МФТИ (с 2022 года), на которой работает с 2004 года; главный научный сотрудник отдела «Интеллектуальные системы» ВЦ ФИЦ ИУ РАН, где работает с 2001 года. Окончил МФТИ (1994) и аспирантуру ВЦ РАН (1997). Кандидатская диссертация «Локальные базисы в алгебраическом подходе к проблеме распознавания» (1999, специальность 01.01.09); докторская диссертация «Комбинаторная теория надёжности обучения по прецедентам» (2010, специальность 05.13.17). Автор комбинаторной теории надёжности обучения по прецедентам, оценивающей вероятность переобучения с учётом эффектов расслоения и сходства в семействах алгоритмов; основное направление его исследований — теория и методы аддитивной регуляризации тематических моделей (ARTM). С 2014 года — научный руководитель разработки библиотеки тематического моделирования с открытым кодом BigARTM, с 2019 года — поисково-рекомендательной системы SciSearch. Один из идеологов и администраторов ресурса MachineLearning.ru (с 2008 года). Медаль РАН с премией для молодых учёных (1999, совместно с Е. В. Нарыжным); почётное звание профессора РАН (2015); юбилейная медаль РАН «300 лет Российской академии наук» (2024); премия Yandex ML Prize в номинации «Зал славы» (2025). Автор курсов «Вероятностные тематические модели», «Математические методы анализа текстов», «Прикладной статистический анализ данных» и спецкурса по теории надёжности обучения по прецедентам; в 2008–2020 годах преподавал в Школе анализа данных Яндекса. С 2004 года читает годовой курс «Машинное обучение» в МФТИ и ВМК МГУ.

Vector calculus. Green's theorem

2026Problem sheetSergey

A contour integral as a double integral: the hypotheses of Green's theorem, orientation of the contour, area as a contour integral, closing an open arc, an annulus, a singular point inside the contour.
0
260