01_MMM |
Introduction
- What is Machine Learing
- Computational environment
- AI assisted programming |
- Burkov, A. (2019) The One Hundred Page Machine Learning Book pp. XX-XX
- Rashid, Tariq. (2016) Make Your Own Neural Network. Kindle Edition. pp XX-XX
- Machine Learning Theory and Algorithms pp 19-22
- Machine Learning Techniques for Civil Engineering Problems |
01_MMM |
Exploratory Data Analysis |
- Tukey, J (1977). Exploratory Data Analysis. Addison-Wessley (Copy) |
01_MMM |
Data Preparation |
- Chan, J. “Learn Python in One Day and Learn It Well. Python for Beginners with Hands-on Projects.”
- Read a file line by line
- Read a file line by line (PLR approach)
- Reading and writing files
- Python Files I/O
- Working with files in Python
- File handling in Python
- File operations in Python
- How to read a text file from a URL in Python
- Downloading files from web using Python
- An Efficient Way to Read Data from the Web Directly into Python without having to download it to your hard drive
- Web Requests with Python (using http and/or https)
- Troubleshooting certificate errors (really common with https requests) |
01_MMM |
Linear Regression
- Ordinary Least Squares |
- Burkov, A. (2019) The One Hundred Page Machine Learning Book pp. 21-25
- Rashid, Tariq. (2016) Make Your Own Neural Network. Kindle Edition. pp XX-XX
- Chan, Jamie. Machine Learning With Python For Beginners: A Step-By-Step Guide with Hands-On Projects pp. 106-118. |
01_MMM |
Linear Regression
- topic
- topic |
- StructuraL Response Prediction Engine to Support Advanced Seismic Risk Assessment
- Two-Stage Degradation Assessment and Prediction Method for Aircraft Engines…
- Machine Learning Theory and Algorithms pp 22-27
- Applying Regression Analysis to Predict and Classify Construction Cycle Time |
01_MMM |
Optimization Methods
- Search Methods
- Gradient Methods |
- Applied Dynamic Programming Bellman and Dreyfus (1962) pp. 1-21
- Hooke, R., & Jeeves, T. A. (1961). “Direct Search” Solution of Numerical and Statistical Problems. Journal of the ACM, 8(2), 212–229.
- Numerical Optimization by Nocedal & Wright – Chapter 9
- Scipy Optimize Documentation
- Powell, M. J. D. (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal, 7(2), 155–162.
- Nelder, J.A., and Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308–313.
- Press, William H.; Teukolsky, Saul A.; Vetterling, William T.; Flannery, Brian P. (1986). Numerical Recipes: The Art of Scientific Computing. 1ed. New York: Cambridge University Press. pp. 402-406 ISBN 0-521-30811-9. |
01_MMM |
Non-Linear Regression
- Low Dimensional Methods |
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01_MMM |
Non-Linear Regression
- High Dimensional Methods |
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01_MMM |
Logistic Regression
- Logit Regression (binary classes)
- Multiple logistic regression
- Poisson Regression |
- Stojiljković, M.”Logistic Regression in Python”
- Navlani, A. “Understanding Logistic Regression in Python”
- Tripathi, M. “Understanding Logistic Regression with Python: Practical Guide 1”
- Long, A. “Understanding Data Science Classification Metrics in Scikit-Learn in Python”
- “Example of Logistic Regression in Python”
- Li, S. “Building A Logistic Regression in Python, Step by Step”
- Waseem, M. “How To Perform Logistic Regression In Python?”
- Dhiraj, K. “Logistic Regression in Python Using Scikit-learn”
- “ML : Logistic Regression using Python”
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01_MMM |
Decision Trees
- Classification And Regression Trees
- Random Forests (CART Bagging) |
- Decision Trees and Random Forests
- Powerful Guide to learn Random Forest (with codes in R & Python)
- Simplified Introduction to Random Forests
- Using Random Forests in Python with Scikit-Learn
- Random Forest Regression in Python
- Random Forest Algorithm with Python and Scikit-Learn |
01_MMM |
Nearest Neighbor Methods
- Distance Measures
K-nearest neighbor |
- Robinson, S. “K-Nearest Neighbors Algorithm in Python and Scikit-Learn”
- Brownlee, J. “Develop k-Nearest Neighbors in Python From Scratch”
- Brownlee, J. “Four Distance Measures for Machine Learning”
- Navlani, A. “KNN Classification using Scikit-learn”
- Zoltan, C. “KNN in Python”
- Maklin, C. “K Nearest Neighbor Algorithm In Python”
- “k-nearest neighbor algorithm in Python” |
01_MMM |
Support Vector Methods
- Maximum Margin Classification
- Kernel Trick and Common Kernels
- Support Vector Regression |
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01_MMM |
Validation Methods
- Train/Test Split and Holdout Sets
- k-Fold Cross-Validation
- Information Metrics |
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01_MMM |
Clustering Methods
- K-Means and Variants
- Hierarchical Clustering
- Density-Based Clustering |
- IBM “Clustering” in Unsupervised Learning |
01_MMM |
Dimension Reduction Methods
- Principal Components
- Stochastic Embedding
- Discriminant Analysis |
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01_MMM |
Classification Feature Engineering
- one-hot
- ordinal
- embeddings |
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01_MMM |
Predictive Feature Engineering
- Scaling
- Normalization
- Interaction(s) |
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01_MMM |
Neural Networks
- Deep Learners
- Image Analysis (Flat) |
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01_MMM |
Neural Networks
- Convolutional Masking
- Image Classification |
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01_MMM |
Neural Networks
- Stand-alone Packages |
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01_MMM |
Ensemble Methods
- Solution stacking
- Solution Blending
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01_MMM |
Ensemble Methods
- Bagging
- Boosting |
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01_MMM |
Time Series Tools
- Stationarity and Differencing
- Auto/Serial correlation
- ARIMA and SARIMA Models |
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01_MMM |
Time Series Tools
- Seasonal Decomposition
- Exponential Smoothing
- TS Feature Engineering |
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