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Course Outline
1. Understanding Classification Using Nearest Neighbors for Government
- The kNN Algorithm
- Calculating Distance
- Choosing an Appropriate k
- Preparing Data for Use with kNN
- Why is the kNN Algorithm Lazy?
2. Understanding Naive Bayes for Government
- Basic Concepts of Bayesian Methods
- Probability
- Joint Probability
- Conditional Probability with Bayes' Theorem
- The Naive Bayes Algorithm
- The Naive Bayes Classification
- The Laplace Estimator
- Using Numeric Features with Naive Bayes
3. Understanding Decision Trees for Government
- Divide and Conquer
- The C5.0 Decision Tree Algorithm
- Choosing the Best Split
- Pruning the Decision Tree
4. Understanding Classification Rules for Government
- Separate and Conquer
- The One Rule Algorithm
- The RIPPER Algorithm
- Rules from Decision Trees
5. Understanding Regression for Government
- Simple Linear Regression
- Ordinary Least Squares Estimation
- Correlations
- Multiple Linear Regression
6. Understanding Regression Trees and Model Trees for Government
- Adding Regression to Trees
7. Understanding Neural Networks for Government
- From Biological to Artificial Neurons
- Activation Functions
- Network Topology
- The Number of Layers
- The Direction of Information Travel
- The Number of Nodes in Each Layer
- Training Neural Networks with Backpropagation
8. Understanding Support Vector Machines for Government
- Classification with Hyperplanes
- Finding the Maximum Margin
- The Case of Linearly Separable Data
- The Case of Non-Linearly Separable Data
- Using Kernels for Non-Linear Spaces
9. Understanding Association Rules for Government
- The Apriori Algorithm for Association Rule Learning
- Measuring Rule Interest – Support and Confidence
- Building a Set of Rules with the Apriori Principle
10. Understanding Clustering for Government
- Clustering as a Machine Learning Task
- The k-Means Algorithm for Clustering
- Using Distance to Assign and Update Clusters
- Choosing the Appropriate Number of Clusters
11. Measuring Performance for Classification for Government
- Working with Classification Prediction Data
- A Closer Look at Confusion Matrices
- Using Confusion Matrices to Measure Performance
- Beyond Accuracy – Other Measures of Performance
- The Kappa Statistic
- Sensitivity and Specificity
- Precision and Recall
- The F-Measure
- Visualizing Performance Tradeoffs
- ROC Curves
- Estimating Future Performance
- The Holdout Method
- Cross-Validation
- Bootstrap Sampling
12. Tuning Stock Models for Better Performance for Government
- Using Caret for Automated Parameter Tuning
- Creating a Simple Tuned Model
- Customizing the Tuning Process
- Improving Model Performance with Meta-Learning
- Understanding Ensembles
- Bagging
- Boosting
- Random Forests
- Training Random Forests
- Evaluating Random Forest Performance
13. Deep Learning for Government
- Three Classes of Deep Learning
- Deep Autoencoders
- Pre-Trained Deep Neural Networks
- Deep Stacking Networks
14. Discussion of Specific Application Areas for Government
21 Hours
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Very flexible.
