Machine Learning for Engineers

Part I Fundamentals

1.0 Introduction

1.1. Where machine learning can help engineers

1.2. Where machine learning cannot help engineers

1.3. Machine learning to correct idealized models

2. The Landscape of machine learning

2.1. 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 models

5. Clustering data

5.1. Singular value decomposition

5.2. Case Study: SVD to standardize several time series

5.3. K-means

5.4. K-nearest neighbors

5.5. t-SNE

5.6. Case Study: The reflectance spectrum of different foliage

Part II Deep Neural Networks

6. Feed-Forward Neural Networks

6.1. Neurons

6.2. Dropout

6.3. Backpropagation

6.4. Initialization

6.5. Regression

6.6. Classification

6.7. Case Study: The strength of concrete as a function of age and ingredients

7. Convolutional Neural Networks

7.1. Convolutions

7.2. Pooling

7.3. Residual networks

7.4. Case Study: Finding volcanoes on Venus

8. Recurrent neural networks for time series data

8.1. Basic Recurrent neural networks

8.2. Long-term, Short-Term memory

8.3. Attention networks

8.4. Case Study: Predicting future system performance

Part III Advanced Topics in Machine Learning

9. Unsupervised Learning with Neural Networks

9.1. Auto-encoders

9.2. Boltzmann machines

9.3. Case study: Optimization using Inverse models

10. Reinforcement learning

10.1. Case study: controlling a mechanical gantry

11. Transfer learning

11.1. Case study: Transfer learning a simulation emulator for experimental measurements

Part IV Appendices

A. SciKit-Learn

B. Tensorflow