Unsupervised Learning in Machine Learning

TEACHING UNIT 1. INTRODUCTION TO UNSUPERVISED LEARNING Fundamentals of machine learning and differences from supervised learning Objectives, principles, and characteristics of unsupervised learning Applications in industry, science, and emerging technological fields Advantages, limitations, and operational challenges of unsupervised learning TEACHING UNIT 2. DATA PREPROCESSING FOR UNSUPERVISED MODELS Normalization, standardization, and handling of missing data Variable encoding and structural preparation of datasets Feature selection and basic dimensionality reduction Best practices in preprocessing for unsupervised algorithms TEACHING UNIT 3. CLUSTERING: FUNDAMENTALS AND K-MEANS Essential concepts of clustering: distances, centroids, and structure Internal workings of the K-Means algorithm Selecting the optimal number of clusters Conditions for success, limitations, and scenarios where K-Means fails TEACHING UNIT 4. ADVANCED CLUSTERING METHODS DBSCAN: density, epsilon, min_samples, and use cases Hierarchical clustering and Mean Shift: principles and advantages Comparison with K-Means and appropriate parameter selection Visualization, interpretation, and professional applications TEACHING UNIT 5. DIMENSIONALITY REDUCTION: PCA AND RELATED TECHNIQUES Fundamentals of dimensionality reduction and minimum information loss PCA: decomposition, principal components, and visualization t-SNE and UMAP: nonlinear structures and high-dimensional representation Application of these techniques for analyzing and improving unsupervised models TEACHING UNIT 6. GENERATIVE MODELS AND AUTOENCODERS Key concepts of generative models in unsupervised learning Autoencoders: architecture, training, and variants Professional applications: compression, reconstruction, and anomalies Synthetic data generation and uses in regulated industries LEARNING UNIT 7. ANOMALY DETECTION Types of anomalies and their relevance in critical environments Methods based on clustering, distance, and density Autoencoders and statistical approaches to anomalies Applications in cybersecurity, finance, and predictive maintenance TEACHING UNIT 8. EVALUATION OF UNSUPERVISED MODELS Challenges of evaluation without labels and internal approaches Key metrics: Silhouette, Davies-Bouldin, and others Indirect evaluation, cross-validation, and benchmarking Professional interpretation and decision-making based on unsupervised models Common professional mistakes in unsupervised projects and their real-world consequences TEACHING UNIT 9. PRACTICAL APPLICATIONS AND FINAL PROJECTS Consulting-style final project