{"id":15950723,"url":"https://github.com/gerdm/machine-learning","last_synced_at":"2025-08-26T22:05:16.771Z","repository":{"id":88732845,"uuid":"197437941","full_name":"gerdm/machine-learning","owner":"gerdm","description":"Módulo IV: Machine Learning","archived":false,"fork":false,"pushed_at":"2020-04-29T11:29:02.000Z","size":4556,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-04T04:41:26.730Z","etag":null,"topics":["machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gerdm.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-07-17T17:56:36.000Z","updated_at":"2022-04-20T13:46:07.000Z","dependencies_parsed_at":null,"dependency_job_id":"157b4f9b-3c71-47ff-9f15-3cea87be42fd","html_url":"https://github.com/gerdm/machine-learning","commit_stats":{"total_commits":38,"total_committers":2,"mean_commits":19.0,"dds":0.4473684210526315,"last_synced_commit":"d95699f9cb645bdc859e03d46299153959645424"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/gerdm/machine-learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fmachine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fmachine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fmachine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fmachine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gerdm","download_url":"https://codeload.github.com/gerdm/machine-learning/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fmachine-learning/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259360728,"owners_count":22845817,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning"],"created_at":"2024-10-07T13:00:09.086Z","updated_at":"2025-06-11T23:03:19.997Z","avatar_url":"https://github.com/gerdm.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Módulo IV: Machine Learning\n\nEl curso tiene como objetivo introducir la teoría, los conceptos y las prácticas del aprendizaje de máquina.\n\n## Temario\n1. Introducción al aprendizaje de máquina\n    1. ¿Aprendizaje de máquina o inteligencia artificial? \n    2. Nociones básicas\n    3. Definición y motivación para métodos de aprendizaje\n    4. La regresión lineal\n    5. *Overfitting* y *underfitting*\n2. Selección y entrenamiento de modelos\n    1. Cross-Validation\n    2. Regularización L1 \u0026 L2\n    3. El método del gradiente descendente\n    4. Normalización\n3. Modelos supervisados\n    1. La regresión logística\n    2. Análisis de errores\n    3. Árboles de decisión\n    4. Máquinas de soporte vectorial\n    5. Ensemble Learning\n        1.  Random Forests\n        2. Votos de clasificadores\n        3. Bagging\n        4. Boosting\n4. Modelos no supervisados\n    1. K-Nearest Neighbors\n    2. K-means\n    3. Cálculo de la densidad de kernel \n    4. Reducción de dimensiones\n        1. PCA\n        2. T-SNE\n    5. Construcción de curvas de tasas de interés ajustadas por colateral\n    6. Volatilidades implícitas\n5. Aprendizaje de máquina para series de tiempo\n    1. Series de tiempo\n    2. Series de tiempo como un problema de aprendizaje de máquina\n    3. validación de modelos de series de tiempo\n6. Aplicaciones\n    1. Detección de fraudes en un ámbito no supervisado\n    2. Distribución de rendimientos\n    3. Perfilamiento de inversionistas\n    4. VaR mediante aprendizaje de máquina\n    5. Medición del riesgo de crédito y riesgo de contraparte\n    6. Estimación de *Credit Value Adjustment* (CVA)\n7. Optimización de portafolios\n    1. Modelos dinámicos convexos\n    2. Restricciones de régimen de inversión\n    3. Restricciones no genéricas:\n        1. Apalancamiento\n        2. Liquidez\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgerdm%2Fmachine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgerdm%2Fmachine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgerdm%2Fmachine-learning/lists"}