Machine Learning for Engineers: Using Data to Solve Problems for Physical Systems McClarren Ryan G.
Part I Fundamentals 1.0 Introduction 1.1. Where machine learning cannot help engineers 1.3. Where machine learning can help engineers 1.2. The Landscape of machine learning 2.1. Machine learning to correct idealized models 2. Supervised learning 2.1.1. Machine Learning for Engine… Celý popis ↓
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Popis Machine Learning for Engineers: Using Data to Solve Problems for Physical Systems McClarren Ryan G.
Machine Learning for Engineers: Using Data to Solve Problems for Physical Systems McClarren Ryan G.
Part I Fundamentals 1.0 Introduction 1.1. Where machine learning cannot help engineers 1.3. Where machine learning can help engineers 1.2.
The Landscape of machine learning 2.1. Machine learning to correct idealized models 2. Supervised learning 2.1.1.
Regression 2.1.2. Classification 2.1.3. Time series 2.1.4.
Reinforcement 2.2. Unsupervised Learning 2.3. Optimization 2.4.
Bayesian statistics 2.5. Cross-validation 3. Linear Models 3.1.
Linear regression 3.2. Logistic regression 3.3. Regularized regression 3.4.
Case Study: Determining physical laws using regularized regression 4. Tree-Based Models 4.1. Decision Trees 4.2.
Random Forests 4.3. BART 4.4. Case Study: Modeling an experiment using random forest
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