Luis R. Izquierdo
5. Práctica (opcional) de series temporales con Weka
Introduction to Metaheuristics (8/9). Local search applied to the Travelling Salesman Problem
Introduction to Metaheuristics (9/9). Summary of Introduction to Metaheuristics
Introduction to Metaheuristics (7/9). Local search
Introduction to Metaheuristics (6/9). Random search
Introduction to Metaheuristics (5/9). Exploration and Exploitation. When to use metaheuristics
Introduction to Metaheuristics (4/9). Classification criteria for metaheuristics
Introduction to Metaheuristics (3/9). Exact methods, approximate methods and metaheuristics
Introduction to Metaheuristics (2/9). Combinatorial Optimization problems
Introduction to Metaheuristics (1/9)
The scheduling problem (7/7). Computation of makespan in a permutation flow shop
The scheduling problem (6/7). Different priority rules applied to a job shop problem
The scheduling problem (5/7). Different approaches to deal with scheduling problems
The scheduling problem (4/7). Performance measures and objectives
The scheduling problem (3/7). Assumptions and notation
The scheduling problem (2/7). Types of scheduling problems
The scheduling problem (1/7). Introduction
4. Introduction to time series analysis and forecasting using Machine Learning (4/4)
3. Introduction to time series analysis and forecasting using Machine Learning (3/4)
2. Introduction to time series analysis and forecasting using Machine Learning (2/4)
1. Introduction to time series analysis and forecasting using Machine Learning (1/4)
4. Introducción al análisis de series temporales con Machine Learning (4/4)
3. Introducción al análisis de series temporales con Machine Learning (3/4)
2. Introducción al análisis de series temporales con Machine Learning (2/4)
1. Introducción al análisis de series temporales con Machine Learning (1/4)
6. Regularization and model selection
6. Regularización y selección de modelos
5. Estimating test error. Validation and Cross-validation
5. Estimación del error de test. Validación y validación cruzada
4. The bias-variance tradeoff