Uczenie maszynowe

Machine learning · id ml

Podstawowe zadania i metody uczenia z danych. Deep learning ma osobną gałąź dla czytelności.

Core learning tasks and methods. Deep learning sits in its own branch for readability.

In one sentence

To uczenie programów rozpoznawania wzorców w danych, żeby mogły przewidywać, porządkować informacje lub proponować wybory.

This teaches software to recognise patterns in data so it can predict, organise information or suggest choices.

Problem it solves

Trudno ręcznie zapisać wszystkie reguły dla sytuacji, które różnią się szczegółami i pojawiają się w dużej liczbie.

It is hard to write every rule by hand for situations that vary in detail and occur in large numbers.

What it does

Wykorzystuje przykłady do odkrywania zależności, a ich przydatność sprawdza na danych, których model nie używał do nauki.

It uses examples to discover relationships and checks their usefulness on data the model did not use for learning.

Everyday example

System sklepu przewiduje popyt, porządkuje podobne produkty i wybiera propozycje na podstawie wcześniejszych zakupów oraz danych o towarach.

A shop system predicts demand, groups similar products and selects suggestions using previous purchases and product information.

This concerns you if you…

• masz wiele przykładów i chcesz znaleźć wspólne wzorce
• potrzebujesz przewidywań lub porządkowania nowych danych
• have many examples and want to find shared patterns
• need predictions or ways to organise new data

Subtopics

• Drzewa i metody zespołowe (Tree ensembles): Combining simpler models into a stronger predictor.
  • Drzewa decyzyjne (Decision trees): A model that asks a series of if–then questions about features until it reaches an answer. Easy to read but a single tree overfits easily; it is the building block of random forests and boosting.
  • Gradient boosting (XGBoost, LightGBM) (Gradient-boosted trees): Trees added one after another, each correcting the errors of the previous ones. For years the standard for tabular data and competitions.
  • Las losowy i bagging (Random forests and bagging): Many trees trained on random samples of rows and features, with their votes averaged. A robust out-of-the-box model that is not very sensitive to settings.
  • Stacking i łączenie modeli (Stacking and model blending): Stacked generalisation: a meta-model trained on out-of-fold predictions of several base models.
• Prognozy i anomalie (Forecasting and anomaly detection): Predicting change and spotting unusual events.
  • Klasyczne prognozowanie szeregów czasowych (Classical time-series forecasting): Statistical models of trend, seasonality and dependence on past values; still a strong baseline for business forecasting.
  • Modele bazowe dla szeregów czasowych (Time-series foundation models): Transformers pretrained on a very large number of diverse series that forecast a new series without training on it (zero-shot).
  • Wykrywanie anomalii i nowości (Outlier and novelty detection): Finding examples that do not fit the rest of the data (outliers) or what the model has seen before (novelties), e.g. fraud, failures or unusual readings.
• Przygotowanie cech (feature engineering) (Feature engineering and preprocessing): Turning raw columns into features a model can learn from: scaling, encoding categories, imputing missing values, creating and selecting features. Steps are chained in a pipeline so no test-set information leaks in.
  • Kodowanie zmiennych kategorycznych (Categorical feature encoding): Converting categorical features into numeric representations: one-hot, ordinal, target encoding or learned embeddings.
  • Selekcja cech (Feature selection): Choosing a feature subset with filter methods (tests, mutual information), wrapper methods (RFE, sequential selection) and embedded methods (L1, feature importance).
  • Skalowanie i normalizacja cech (Feature scaling and normalisation): Transformations of numeric features: standardisation, min–max, robust scaling, and log, power and quantile transforms.
  • Uzupełnianie brakujących danych (imputacja) (Missing data imputation): Replacing missing entries with estimates from simple statistics, neighbours or a model of other features, often with an added missingness indicator.
• Systemy rekomendacyjne (Recommendation systems): Matching content or products to a person and context.
  • Filtrowanie kolaboracyjne i faktoryzacja macierzy (Collaborative filtering and matrix factorisation): Recommending from the behaviour of similar people (“who bought X also bought Y”); matrix factorisation learns user and item embeddings.
  • Generowanie kandydatów, scoring i re-ranking (Candidate generation, scoring and re-ranking): The typical architecture of large systems: quickly narrow a huge catalogue to hundreds of candidates, score them more precisely, then account for freshness, diversity and business rules.
  • Rekomendacje oparte na treści (Content-based filtering): Recommending items similar to what a person already likes, based on features of the items themselves.
• Uczenie nadzorowane (Supervised learning): Learning from examples with the answer attached.
  • Maszyny wektorów nośnych (SVM) (Support vector machines): A classifier that looks for the boundary with the widest margin between classes; kernels (the kernel trick) let it draw non-linear boundaries.
  • Metoda k najbliższych sąsiadów (k-NN) (k-nearest neighbours): A model with no parameter training: the answer for a new example comes from the k most similar examples in the data. The same idea underlies vector search.
  • Modele bazowe dla danych tabelarycznych (Tabular foundation models): Transformers pretrained on millions of (mostly synthetic) tables that predict on a new table without training, learning in context from the given rows. A new direction (since 2025) competing with gradient boosting on small and medium datasets.
  • Naiwny klasyfikator Bayesa (Naive Bayes classifiers): A probabilistic classifier built on Bayes' theorem with the simplifying assumption that features are independent. A fast baseline, classically for text (e.g. spam filtering).
  • Regresja kwantylowa i przedziały predykcji (Quantile regression and prediction intervals): Estimating conditional quantiles of the target by minimising the pinball loss; a pair of quantiles forms a prediction interval.
  • Regresja liniowa i logistyczna (Linear and logistic regression): The simplest predictive models: a weighted sum of features gives a number (linear regression) or a class probability (logistic regression). They are the usual place to learn loss, gradient descent and decision thresholds.
  • Uczenie rankingu (learning to rank) (Learning to rank): Learning a scoring function for documents given a query, with pointwise, pairwise or listwise objectives that optimise ranking-quality metrics.
• Uczenie nienadzorowane (Unsupervised learning): Finding structure without ready labels.
  • Klasteryzacja (Clustering): Grouping similar examples without labels, e.g. customer segmentation or organising documents.
    • Klasteryzacja gęstościowa (DBSCAN, HDBSCAN) (Density-based clustering): Clusters are dense regions of data; points in sparse areas are marked as noise. No need to set the number of clusters, and it handles irregular shapes.
    • Klasteryzacja hierarchiczna (Hierarchical clustering): Builds a tree of groups by gradually merging the closest ones (or splitting the largest); the cut level is chosen from a dendrogram.
    • Metoda k-średnich (k-means) (k-means clustering): Splits data into a preset number k of groups around centroids that are moved iteratively. Works well for compact groups of similar size.
  • Modelowanie tematów (Topic modelling): Unsupervised discovery of themes in a corpus: probabilistic word mixtures (LDA) or clustering of embeddings with topic descriptions (BERTopic).
  • Redukcja wymiarowości (Dimensionality reduction): Turning many features into a few that keep the most important structure of the data — for visualisation, compression or as a step before another model.
    • Analiza głównych składowych (PCA) (Principal component analysis): A linear method that finds the directions of largest variance in the data and projects the data onto them.
    • Mapy danych w 2D: t-SNE i UMAP (Nonlinear embedding for visualisation (t-SNE, UMAP)): Non-linear methods that lay points out in 2D so that neighbours stay close. Popular for inspecting embeddings and clusters; distances between groups on such maps are easy to over-interpret.
  • Reguły asocjacyjne i częste zbiory (Association rules and frequent itemsets): Mining frequent itemsets and X→Y rules in transactional data, scored by support, confidence and lift.
• Uczenie przyrostowe (online) na strumieniach danych (Online and incremental learning on data streams): Updating a model after single samples or small chunks of a stream, with predict-then-learn evaluation and reaction to concept drift.
• Uczenie wielozadaniowe (Multi-task learning): Joint training on several tasks with a shared representation, acting as regularisation and knowledge transfer between tasks.
• Uczenie z ograniczonym lub automatycznym nadzorem (Learning with limited or automatically derived supervision): Learning paradigms where supervision is partial, generated automatically from data, or attached to groups rather than individual examples.
  • Uczenie pół-nadzorowane (Semi-supervised learning): Combining a small labelled set with a large unlabelled one through pseudo-labels, consistency regularisation or label propagation over a similarity graph.
    • Samouczenie i pseudoetykiety (Self-training and pseudo-labelling): Iteratively adding the model's above-threshold predictions to training as labels; modern variants combine it with augmentation and consistency.
  • Uczenie samonadzorowane (Self-supervised learning): Learning representations from a signal generated by the data itself (pretext tasks, contrast, masking), later transferred to downstream tasks.
  • Uczenie wieloinstancyjne (MIL) (Multiple instance learning (MIL)): Learning from labels attached to bags of instances; the model aggregates instance representations, e.g. with attention, into a bag-level prediction.
• Walidacja i dobór modelu (Model selection and validation): How to check reliably whether a model generalises to new data, and how to choose a model and its settings without fooling yourself.
  • AutoML — automatyczny dobór modeli (Automated machine learning (AutoML)): Tools that prepare features, try many models and settings, and ensemble the best ones automatically.
  • Przeuczenie, niedouczenie i regularyzacja (Overfitting, underfitting and regularisation): The trade-off between a model that is too simple (misses the pattern) and one fitted too closely to training data (learns noise), and techniques that counter it.
  • Strojenie hiperparametrów (Hyperparameter tuning): Searching for model settings that are not learned from data (tree depth, regularisation strength, learning rate) using validation results.
  • Walidacja krzyżowa (Cross-validation): Repeatedly splitting data into training and validation parts (e.g. 5 folds) so the estimate does not depend on one lucky split.

Courses

• Machine Learning Specialization

Sources

• Stanford CS229 · Machine Learning (2018, archiwum) · reference: Klasyczne ML, statystyka i podstawy uczenia. Użyte jako stabilny szkielet, nie przegląd nowości.
• Google · Recommendation Systems · reference: Rekomendacje, retrieval, scoring i reranking.
• DeepLearning.AI — course catalogue · course catalogue: 42 topic labels with counts, about 130 courses.