Artificial Neural Networks, Machine Learning and Deep Thinking Training Course

1. Understanding Classification Using Nearest Neighbors for Government

• The k-Nearest Neighbors (kNN) Algorithm
• Calculating Distance Metrics
• Selecting an Appropriate Value for k
• Preparing Data for Use with kNN in Public Sector Applications
• Why is the kNN Algorithm Considered Lazy?

2. Understanding Naive Bayes for Government

• Basic Concepts of Bayesian Methods
• Fundamentals of Probability
• Joint Probability in Data Analysis
• Conditional Probability with Bayes' Theorem
• The Naive Bayes Algorithm and Its Applications
• Naive Bayes Classification Techniques
• The Laplace Estimator for Smoothing Probabilities
• Using Numeric Features with the Naive Bayes Classifier

3. Understanding Decision Trees for Government

• The Divide and Conquer Strategy in Decision Making
• The C5.0 Decision Tree Algorithm for Data Analysis
• Selecting the Best Split Criteria
• Pruning Techniques to Enhance Model Accuracy

4. Understanding Classification Rules for Government

• The Separate and Conquer Approach in Rule-Based Systems
• The One Rule Algorithm for Simple Classifications
• The RIPPER Algorithm for Generating Complex Rules
• Deriving Rules from Decision Trees for Improved Accuracy

5. Understanding Regression Analysis for Government

• Simple Linear Regression Models
• Ordinary Least Squares Estimation Techniques
• Correlation Analysis in Data Sets
• Multiple Linear Regression for Multivariate Analysis

6. Understanding Regression Trees and Model Trees for Government

• Incorporating Regression into Tree-Based Models

7. Understanding Neural Networks for Government

• From Biological to Artificial Neurons in Computational Models
• Activation Functions and Their Role in Neural Networks
• Network Topology and Architecture Design
• Determining the Number of Layers in a Neural Network
• The Direction of Information Flow in Neural Networks
• Optimizing the Number of Nodes in Each Layer
• Training Neural Networks Using Backpropagation Techniques

8. Understanding Support Vector Machines for Government

• Classification Using Hyperplanes for Data Separation
• Finding the Maximum Margin for Optimal Classification
• Handling Linearly Separable Data in SVMs
• Addressing Non-Linearly Separable Data with Kernels
• Using Kernels to Map Data into Higher-Dimensional Spaces

9. Understanding Association Rules for Government

• The Apriori Algorithm for Learning Association Rules
• Evaluating Rule Interest Using Support and Confidence Metrics
• Building a Comprehensive Set of Rules with the Apriori Principle

10. Understanding Clustering Techniques for Government

• Clustering as a Machine Learning Task in Public Sector Applications
• The k-Means Algorithm for Cluster Analysis
• Using Distance Metrics to Assign and Update Clusters
• Selecting the Appropriate Number of Clusters for Optimal Results

11. Measuring Performance in Classification Models for Government

• Working with Classification Prediction Data for Evaluation
• A Closer Look at Confusion Matrices and Their Components
• Using Confusion Matrices to Measure Model Performance
• Beyond Accuracy: Other Measures of Model Effectiveness
• The Kappa Statistic for Measuring Agreement
• Sensitivity and Specificity in Diagnostic Testing
• Precision and Recall Metrics for Classification Models
• The F-Measure for Balancing Precision and Recall
• Visualizing Performance Tradeoffs with Graphs and Plots
• ROC Curves for Evaluating Binary Classifiers
• Estimating Future Model Performance Through Validation Techniques
• The Holdout Method for Model Evaluation
• Cross-Validation Techniques for Robust Model Assessment
• Bootstrap Sampling Methods for Improved Accuracy

12. Tuning Stock Models for Better Performance in Government Applications

• Using the caret Package for Automated Parameter Tuning
• Creating a Simple Tuned Model for Immediate Improvement
• Customizing the Tuning Process to Fit Specific Needs
• Improving Model Performance with Meta-Learning Techniques
• Understanding Ensemble Methods for Enhanced Accuracy
• Bagging Techniques for Reducing Variance
• Boosting Methods for Improving Weak Learners
• Random Forests for Robust Classification and Regression
• Training Random Forest Models for Government Applications
• Evaluating the Performance of Random Forests in Real-World Scenarios

13. Deep Learning for Government

• Three Classes of Deep Learning Architectures
• Deep Autoencoders for Unsupervised Learning
• Pre-Trained Deep Neural Networks for Transfer Learning
• Deep Stacking Networks for Complex Data Analysis

14. Discussion of Specific Application Areas for Government