Machine Learning

Algorithms, models, and techniques for learning patterns from data.

Supervised Learning

Regression, classification, and fundamental supervised learning algorithms.

Unsupervised Learning

Clustering, dimensionality reduction, and learning without labels.

Ensemble Methods

Bagging, boosting, random forests, and combining models for better performance.

Model Evaluation

Metrics, cross-validation, bias-variance tradeoff, and model selection.

Probabilistic Models

Graphical models, Bayesian networks, and probabilistic inference.

Forecasting, anomaly detection, and analysis of temporal data.

Recommender Systems

Collaborative filtering, content-based, and hybrid recommendation methods.

Semi-Supervised Learning

Learning with limited labels using pseudo-labels, consistency, and self-training.

Active Learning

Selecting the most informative samples to label for efficient training.

Online Learning

Streaming data, bandits, regret minimization, and incremental updates.

Anomaly Detection

Outlier detection, novelty detection, and identifying unusual patterns.

Dimensionality Reduction

PCA, t-SNE, UMAP, and projecting high-dimensional data for visualization and modeling.

SVMs, kernel trick, reproducing kernel Hilbert spaces, and nonlinear classification.

Causal Inference

Causal graphs, do-calculus, treatment effects, and counterfactual reasoning.