Introduction
This course introduces students to the core principles and methods of pattern recognition, with a particular emphasis on statistical learning techniques for classification (30 hours). It provides a solid theoretical foundation complemented by practical implementation using Python and the Scikit-learn library. Students will learn to evaluate classification models and apply them to real-world data.
- Luis Baumela, 15h
- Roberto Valle, 15h
Unit 1: Introduction to Pattern Recognition
This unit sets the foundations of the course:
- Definition and scope of pattern recognition.
- Motivation and real-world applications.
- Euclidean distance classifier as a baseline approach.
Unit 2: Statistical Foundations of Classification
Focuses on the probabilistic perspective for classification:
- Bayesian decision theory.
- Parametric generative classifiers (e.g., Gaussian models).
- Non-parametric classifiers (e.g., k-NN, Parzen windows).
Unit 3: Classifier Evaluation
Covers key methods to assess model performance:
- Confusion matrix, accuracy, precision, recall.
- Cross-validation techniques.
- ROC curves and AUC.
Unit 4: Dimensionality Reduction
Techniques to manage high-dimensional data and improve model efficiency:
- Feature selection vs. feature transformation.
- Principal Component Analysis (PCA).
- Linear Discriminant Analysis (LDA).
Unit 5: Discriminative Classifiers
Introduction to widely used discriminative models:
- Logistic regression.
- Support Vector Machines (SVM).
- Decision boundaries and margin-based learning.
Unit 6: Unsupervised Classification
Explores clustering methods for structure discovery in unlabeled data:
- K-means clustering.
- Hierarchical clustering.
- Evaluation of clustering quality.