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Machine Learning by Example for Civil Engineers
Syllabus
1. Introduction
2. Exploratory Data Analysis
3. Data Preparation
4. Linear Regression
5. Optimization Methods
Enumeration Methods: Grid Search
Nelder and Mead Downhill Simplex Method
Hooke and Jeeves Pattern Search
Powell’s Direction Set Method
6. Non-Linear Regression
7. Logistic Regression
8. Decision Trees
8. Nearest Neighbor Methods
9. Support Vectors
10. Validation
11. Clustering
12. Dimension Reduction
13. Feature Engineering
14. Neural Networks
14.1 Artifical Neural Network (Homebrew)
14.2 Artifical Neural Network (Homebrew with OpenAI refactoring)
14.2 Artificial Neural Network using Packages
14.4 Artifical Neural Network to Detect Cracks
14.5 ANN in PyTorch to Detect Cracks
14.6 ANN for Prediction Engines (Regression)
15. Ensemble Methods
16. Time Series Tools
.ipynb
.pdf
12. Dimension Reduction
12. Dimension Reduction
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Course Website
http://54.243.252.9/ce-5319-webroot/
